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Liu W, Zhang Q, Li Q, Wang S. Contrastive and uncertainty-aware nuclei segmentation and classification. Comput Biol Med 2024; 178:108667. [PMID: 38850962 DOI: 10.1016/j.compbiomed.2024.108667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/18/2024] [Accepted: 05/26/2024] [Indexed: 06/10/2024]
Abstract
Nuclei segmentation and classification play a crucial role in pathology diagnosis, enabling pathologists to analyze cellular characteristics accurately. Overlapping cluster nuclei, misdetection of small-scale nuclei, and pleomorphic nuclei-induced misclassification have always been major challenges in the nuclei segmentation and classification tasks. To this end, we introduce an auxiliary task of nuclei boundary-guided contrastive learning to enhance the representativeness and discriminative power of visual features, particularly for addressing the challenge posed by the unclear contours of adherent nuclei and small nuclei. In addition, misclassifications resulting from pleomorphic nuclei often exhibit low classification confidence, indicating a high level of uncertainty. To mitigate misclassification, we capitalize on the characteristic clustering of similar cells to propose a locality-aware class embedding module, offering a regional perspective to capture category information. Moreover, we address uncertain classification in densely aggregated nuclei by designing a top-k uncertainty attention module that leverages deep features to enhance shallow features, thereby improving the learning of contextual semantic information. We demonstrate that the proposed network outperforms the off-the-shelf methods in both nuclei segmentation and classification experiments, achieving the state-of-the-art performance.
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Affiliation(s)
- Wenxi Liu
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
| | - Qing Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
| | - Qi Li
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
| | - Shu Wang
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
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2
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Alahmari SS, Goldgof D, Hall LO, Mouton PR. A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7458-7477. [PMID: 36327184 DOI: 10.1109/tnnls.2022.3213407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate stereological quantification of microscopic structures in stained tissue sections plays a critical role in understanding human diseases and developing safe and effective treatments. In this article, we review the most recent deep learning approaches for cell (nuclei) detection and segmentation in cancer and Alzheimer's disease with an emphasis on deep learning approaches combined with unbiased stereology. Major challenges include accurate and reproducible cell detection and segmentation of microscopic images from stained sections. Finally, we discuss potential improvements and future trends in deep learning applied to cell detection and segmentation.
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3
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Hörst F, Rempe M, Heine L, Seibold C, Keyl J, Baldini G, Ugurel S, Siveke J, Grünwald B, Egger J, Kleesiek J. CellViT: Vision Transformers for precise cell segmentation and classification. Med Image Anal 2024; 94:103143. [PMID: 38507894 DOI: 10.1016/j.media.2024.103143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/14/2024] [Accepted: 03/12/2024] [Indexed: 03/22/2024]
Abstract
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.
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Affiliation(s)
- Fabian Hörst
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), 45147 Essen, Germany.
| | - Moritz Rempe
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), 45147 Essen, Germany
| | - Lukas Heine
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), 45147 Essen, Germany
| | - Constantin Seibold
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Clinic for Nuclear Medicine, University Hospital Essen (AöR), 45147 Essen, Germany
| | - Julius Keyl
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Institute of Pathology, University Hospital Essen (AöR), 45147 Essen, Germany
| | - Giulia Baldini
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen (AöR), 45147 Essen, Germany
| | - Selma Ugurel
- Department of Dermatology, University Hospital Essen (AöR), 45147 Essen, Germany; German Cancer Consortium (DKTK, Partner site Essen), 69120 Heidelberg, Germany
| | - Jens Siveke
- West German Cancer Center, partner site Essen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Essen, University Hospital Essen (AöR), 45147 Essen, Germany; Bridge Institute of Experimental Tumor Therapy (BIT) and Division of Solid Tumor Translational Oncology (DKTK), West German Cancer Center Essen, University Hospital Essen (AöR), University of Duisburg-Essen, 45147 Essen, Germany
| | - Barbara Grünwald
- Department of Urology, West German Cancer Center, 45147 University Hospital Essen (AöR), Germany; Princess Margaret Cancer Centre, M5G 2M9 Toronto, Ontario, Canada
| | - Jan Egger
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), 45147 Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), 45147 Essen, Germany; German Cancer Consortium (DKTK, Partner site Essen), 69120 Heidelberg, Germany; Department of Physics, TU Dortmund University, 44227 Dortmund, Germany
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4
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Fernandez G, Zeineh J, Prastawa M, Scott R, Madduri AS, Shtabsky A, Jaffer S, Feliz A, Veremis B, Mejias JC, Charytonowicz E, Gladoun N, Koll G, Cruz K, Malinowski D, Donovan MJ. Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer. Clin Breast Cancer 2024; 24:93-102.e6. [PMID: 38114366 DOI: 10.1016/j.clbc.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/19/2023] [Accepted: 10/29/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND PreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis. MATERIALS AND METHODS Using hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day, and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility. RESULTS Analytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. CONCLUSION In summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients.
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Affiliation(s)
- Gerardo Fernandez
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | | | | | | | | | | | - Brandon Veremis
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | - Nataliya Gladoun
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | | | - Michael J Donovan
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY; University of Miami, Pathology, Miami, FL.
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Piccinini F, Drudi L, Pyun JC, Lee M, Kwak B, Ku B, Carbonaro A, Martinelli G, Castellani G. Two-dimensional segmentation fusion tool: an extensible, free-to-use, user-friendly tool for combining different bidimensional segmentations. Front Bioeng Biotechnol 2024; 12:1339723. [PMID: 38357706 PMCID: PMC10865367 DOI: 10.3389/fbioe.2024.1339723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Introduction: In several fields, the process of fusing multiple two-dimensional (2D) closed lines is an important step. For instance, this is fundamental in histology and oncology in general. The treatment of a tumor consists of numerous steps and activities. Among them, segmenting the cancer area, that is, the correct identification of its spatial location by the segmentation technique, is one of the most important and at the same time complex and delicate steps. The difficulty in deriving reliable segmentations stems from the lack of a standard for identifying the edges and surrounding tissues of the tumor area. For this reason, the entire process is affected by considerable subjectivity. Given a tumor image, different practitioners can associate different segmentations with it, and the diagnoses produced may differ. Moreover, experimental data show that the analysis of the same area by the same physician at two separate timepoints may result in different lines being produced. Accordingly, it is challenging to establish which contour line is the ground truth. Methods: Starting from multiple segmentations related to the same tumor, statistical metrics and computational procedures could be exploited to combine them for determining the most reliable contour line. In particular, numerous algorithms have been developed over time for this procedure, but none of them is validated yet. Accordingly, in this field, there is no ground truth, and research is still active. Results: In this work, we developed the Two-Dimensional Segmentation Fusion Tool (TDSFT), a user-friendly tool distributed as a free-to-use standalone application for MAC, Linux, and Windows, which offers a simple and extensible interface where numerous algorithms are proposed to "compute the mean" (i.e., the process to fuse, combine, and "average") multiple 2D lines. Conclusions: The TDSFT can support medical specialists, but it can also be used in other fields where it is required to combine 2D close lines. In addition, the TDSFT is designed to be easily extended with new algorithms thanks to a dedicated graphical interface for configuring new parameters. The TDSFT can be downloaded from the following link: https://sourceforge.net/p/tdsft.
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Affiliation(s)
- Filippo Piccinini
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| | - Lorenzo Drudi
- Student, Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Jae-Chul Pyun
- Department of Materials Science and Engineering, Yonsei University, Seoul, Republic of Korea
| | - Misu Lee
- Division of Life Sciences, College of Life Science and Bioengineering, Incheon National University, Incheon, Republic of Korea
- Institute for New Drug Development, College of Life Science and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Bongseop Kwak
- College of Medicine, Dongguk University, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Bosung Ku
- Central R&D Center, Medical and Bio Decision (MBD) Co., Ltd., Suwon, Republic of Korea
| | - Antonella Carbonaro
- Department of Computer Science and Engineering (DISI), University of Bologna, Cesena, Italy
| | - Giovanni Martinelli
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy
| | - Gastone Castellani
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
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Dia AK, Ebrahimpour L, Yolchuyeva S, Tonneau M, Lamaze FC, Orain M, Coulombe F, Malo J, Belkaid W, Routy B, Joubert P, Després P, Manem VSK. The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study. Cancers (Basel) 2024; 16:348. [PMID: 38254838 PMCID: PMC10813866 DOI: 10.3390/cancers16020348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.
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Affiliation(s)
- Abdou Khadir Dia
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
| | - Leyla Ebrahimpour
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Physics, Laval University, Quebec City, QC G1V 0A6, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Sevinj Yolchuyeva
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Marion Tonneau
- Lille Faculty of Medicine, University of Lille, 59020 Lille, France
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Fabien C. Lamaze
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Michèle Orain
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Francois Coulombe
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Julie Malo
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Wiam Belkaid
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Bertrand Routy
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Philippe Joubert
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Philippe Després
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Physics, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Venkata S. K. Manem
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
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Lan X, Guo G, Wang X, Yan Q, Xue R, Li Y, Zhu J, Dong Z, Wang F, Li G, Wang X, Xu J, Jiang Y. Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features. Skin Res Technol 2024; 30:e13571. [PMID: 38196164 PMCID: PMC10776893 DOI: 10.1111/srt.13571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 12/23/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity. OBJECTIVES To develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high-risk (HR) and low-risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors. METHODS The deep learning systems were trained on 161 H&E -stained sections which contained 51 sections of HR-BCC, 50 sections of LR-BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi-head self-attention (MSA) U-Net, nuclei segmentation by HoVer-Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers. RESULTS MSA-U-Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977 ± 0.0159, 0.955 ± 0.0181, 0.885 ± 0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T-T_meanEdgeLength, T-T_Nsubgraph, S-T_HarmonicCentrality, S-S_Degrees. The risk stratification model can well predict HR-BCC and LR-BCC with 0.920 ± 0.0579, 0.839 ± 0.0176, 0.825 ± 0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR-BCC and LR-BCC comprised IntensityMin, Solidity, T-T_minEdgeLength, T-T_Coreness, T-T_Degrees, T-T_Betweenness, S-T_Degrees. CONCLUSIONS This framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.
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Affiliation(s)
- Xuemei Lan
- Department of DermatopathologyHospital for Skin DiseasesInstitute of DermatologyChinese Academy of Medical Sciences & Peking Union Medical College, NanjingJiangsuChina
| | - Guanchen Guo
- lnstitute for Al in MedicineSchool of Artificial lntelligenceNanjing University of Information Science and TechnologyNanjingChina
| | - Xiaopo Wang
- Department of DermatopathologyHospital for Skin DiseasesInstitute of DermatologyChinese Academy of Medical Sciences & Peking Union Medical College, NanjingJiangsuChina
| | - Qiao Yan
- Department of DermatologySchool of MedicineZhong Da HospitalSoutheast UniversityNanjingChina
| | - Ruzeng Xue
- Dermatology HospitalSouthern Medical UniversityGuangzhouChina
| | - Yufen Li
- Department of DermatopathologyHospital for Skin DiseasesInstitute of DermatologyChinese Academy of Medical Sciences & Peking Union Medical College, NanjingJiangsuChina
| | - Jiaping Zhu
- Department of DermatopathologyHospital for Skin DiseasesInstitute of DermatologyChinese Academy of Medical Sciences & Peking Union Medical College, NanjingJiangsuChina
| | - Zhengbang Dong
- Department of DermatologySchool of MedicineZhong Da HospitalSoutheast UniversityNanjingChina
| | - Fei Wang
- Department of DermatologySchool of MedicineZhong Da HospitalSoutheast UniversityNanjingChina
| | - Guomin Li
- Dermatology HospitalSouthern Medical UniversityGuangzhouChina
| | - Xiangxue Wang
- lnstitute for Al in MedicineSchool of Artificial lntelligenceNanjing University of Information Science and TechnologyNanjingChina
| | - Jun Xu
- lnstitute for Al in MedicineSchool of Artificial lntelligenceNanjing University of Information Science and TechnologyNanjingChina
| | - Yiqun Jiang
- Department of DermatopathologyHospital for Skin DiseasesInstitute of DermatologyChinese Academy of Medical Sciences & Peking Union Medical College, NanjingJiangsuChina
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8
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Du G, Zhang P, Guo J, Pang X, Kan G, Zeng B, Chen X, Liang J, Zhan Y. MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network. J Digit Imaging 2023; 36:2411-2426. [PMID: 37714969 PMCID: PMC10584774 DOI: 10.1007/s10278-023-00890-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 09/17/2023] Open
Abstract
Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate identification and segmentation of muscle fiber boundary is an important prerequisite for the evaluation of skeletal muscle fiber atrophy. However, there are many challenges to segment muscle fiber from immunofluorescence images, including the presence of low contrast in fiber boundaries in immunofluorescence images and the influence of background noise. Due to the limitations of traditional convolutional neural network-based segmentation methods in capturing global information, they cannot achieve ideal segmentation results. In this paper, we propose a muscle fiber segmentation network (MF-Net) method for effective segmentation of macaque muscle fibers in immunofluorescence images. The network adopts a dual encoder branch composed of convolutional neural networks and transformer to effectively capture local and global feature information in the immunofluorescence image, highlight foreground features, and suppress irrelevant background noise. In addition, a low-level feature decoder module is proposed to capture more global context information by combining different image scales to supplement the missing detail pixels. In this study, a comprehensive experiment was carried out on the immunofluorescence datasets of six macaques' weightlessness models and compared with the state-of-the-art deep learning model. It is proved from five segmentation indices that the proposed automatic segmentation method can be accurately and effectively applied to muscle fiber segmentation in shank immunofluorescence images.
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Affiliation(s)
| | - Peng Zhang
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Jianzhong Guo
- Institute of Applied Acoustics, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Xiangsheng Pang
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Guanghan Kan
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Bin Zeng
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Xiaoping Chen
- China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China.
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, 710071, China.
| | - Yonghua Zhan
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China.
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9
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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10
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Iqbal S, Qureshi AN, Alhussein M, Aurangzeb K, Kadry S. A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images. Biomimetics (Basel) 2023; 8:370. [PMID: 37622975 PMCID: PMC10452605 DOI: 10.3390/biomimetics8040370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/26/2023] Open
Abstract
The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
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11
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Pan X, Cheng J, Hou F, Lan R, Lu C, Li L, Feng Z, Wang H, Liang C, Liu Z, Chen X, Han C, Liu Z. SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations. Med Image Anal 2023; 88:102867. [PMID: 37348167 DOI: 10.1016/j.media.2023.102867] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/25/2023] [Accepted: 06/07/2023] [Indexed: 06/24/2023]
Abstract
High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost-Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code is available at: https://github.com/panxipeng/nuclear_segandcls.
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Affiliation(s)
- Xipeng Pan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China.
| | - Jijun Cheng
- Software Engineering Institute, East China Normal University, Shanghai 200062, China
| | - Feihu Hou
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
| | - Rushi Lan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
| | - Cheng Lu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Lingqiao Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
| | - Zhengyun Feng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
| | - Huadeng Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Zhenbing Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, China.
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China.
| | - Zaiyi Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China.
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12
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Martos O, Hoque MZ, Keskinarkaus A, Kemi N, Näpänkangas J, Eskuri M, Pohjanen VM, Kauppila JH, Seppänen T. Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal. Pathol Res Pract 2023; 248:154694. [PMID: 37494804 DOI: 10.1016/j.prp.2023.154694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023]
Abstract
Histological analysis with microscopy is the gold standard to diagnose and stage cancer, where slides or whole slide images are analyzed for cell morphological and spatial features by pathologists. The nuclei of cancerous cells are characterized by nonuniform chromatin distribution, irregular shapes, and varying size. As nucleus area and shape alone carry prognostic value, detection and segmentation of nuclei are among the most important steps in disease grading. However, evaluation of nuclei is a laborious, time-consuming, and subjective process with large variation among pathologists. Recent advances in digital pathology have allowed significant applications in nuclei detection, segmentation, and classification, but automated image analysis is greatly affected by staining factors, scanner variability, and imaging artifacts, requiring robust image preprocessing, normalization, and segmentation methods for clinically satisfactory results. In this paper, we aimed to evaluate and compare the digital image analysis techniques used in clinical pathology and research in the setting of gastric cancer. A literature review was conducted to evaluate potential methods of improving nuclei detection. Digitized images of 35 patients from a retrospective cohort of gastric adenocarcinoma at Oulu University Hospital in 1987-2016 were annotated for nuclei (n = 9085) by expert pathologists and 14 images of different cancer types from public TCGA dataset with annotated nuclei (n = 7000) were used as a comparison to evaluate applicability in other cancer types. The detection and segmentation accuracy with the selected color normalization and stain separation techniques were compared between the methods. The extracted information can be supplemented by patient's medical data and fed to the existing statistical clinical tools or subjected to subsequent AI-assisted classification and prediction models. The performance of each method is evaluated by several metrics against the annotations done by expert pathologists. The F1-measure of 0.854 ± 0.068 is achieved with color normalization for the gastric cancer dataset, and 0.907 ± 0.044 with color deconvolution for the public dataset, showing comparable results to the earlier state-of-the-art works. The developed techniques serve as a basis for further research on application and interpretability of AI-assisted tools for gastric cancer diagnosis.
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Affiliation(s)
- Oleg Martos
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Md Ziaul Hoque
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.
| | - Anja Keskinarkaus
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
| | - Niko Kemi
- Department of Pathology, Oulu University Hospital, Finland, and University of Oulu, Finland
| | - Juha Näpänkangas
- Department of Pathology, Oulu University Hospital, Finland, and University of Oulu, Finland
| | - Maarit Eskuri
- Department of Pathology, Oulu University Hospital, Finland, and University of Oulu, Finland
| | - Vesa-Matti Pohjanen
- Department of Pathology, Oulu University Hospital, Finland, and University of Oulu, Finland
| | - Joonas H Kauppila
- Department of Surgery, Oulu University Hospital, Finland, and University of Oulu, Finland
| | - Tapio Seppänen
- Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
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13
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Das N, Saha S, Nasipuri M, Basu S, Chakraborti T. Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology. PLoS One 2023; 18:e0286862. [PMID: 37352172 PMCID: PMC10289330 DOI: 10.1371/journal.pone.0286862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/24/2023] [Indexed: 06/25/2023] Open
Abstract
Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary in biomedical applications. We present a novel fuzzy water flow scheme that takes the coarse segmentation output of a base deep learning framework to then provide a more fine-grained and instance level robust segmentation output. Our two stage synergistic segmentation method, Deep-Fuzz, works especially well for overlapping objects, and achieves state-of-the-art performance in four public cell nuclei segmentation datasets. We also show through visual examples how our final output is better aligned with pathological insights, and thus more clinically interpretable.
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Affiliation(s)
- Nirmal Das
- Deapartemnt of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, West Bengal, India
- Deapartment of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Satadal Saha
- Department of Electrical and Computer Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India
| | - Mita Nasipuri
- Deapartment of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Subhadip Basu
- Deapartment of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Tapabrata Chakraborti
- University College London and The Alan Turing Institute, London, United Kingdom
- Linacre College, University of Oxford, Oxford, United Kingdom
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14
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Barrera C, Corredor G, Viswanathan VS, Ding R, Toro P, Fu P, Buzzy C, Lu C, Velu P, Zens P, Berezowska S, Belete M, Balli D, Chang H, Baxi V, Syrigos K, Rimm DL, Velcheti V, Schalper K, Romero E, Madabhushi A. Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer. NPJ Precis Oncol 2023; 7:52. [PMID: 37264091 PMCID: PMC10235089 DOI: 10.1038/s41698-023-00403-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/19/2023] [Indexed: 06/03/2023] Open
Abstract
The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).
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Affiliation(s)
- Cristian Barrera
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
| | - Germán Corredor
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | | | - Ruiwen Ding
- Case Western Reserve University, School of Engineering, Cleveland, OH, USA
| | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Christina Buzzy
- Case Western Reserve University, School of Engineering, Cleveland, OH, USA
| | - Cheng Lu
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
| | - Priya Velu
- Weill Cornell Medical College, New York, NY, USA
| | - Philipp Zens
- Institute of Pathology, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Sabina Berezowska
- Institute of Pathology, University of Bern, Bern, Switzerland
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | | | - Han Chang
- Bristol Myers Squibb, New York, NY, USA
| | | | - Konstantinos Syrigos
- School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - David L Rimm
- School of Medicine, Yale University, New Haven, CT, USA
| | | | - Kurt Schalper
- School of Medicine, Yale University, New Haven, CT, USA
| | - Eduardo Romero
- Universidad Nacional de Colombia, Facultad de Medicina, Bogotá, Colombia
| | - Anant Madabhushi
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA.
- VA Medical Center, Atlanta, OH, USA.
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15
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Islam Sumon R, Bhattacharjee S, Hwang YB, Rahman H, Kim HC, Ryu WS, Kim DM, Cho NH, Choi HK. Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology. Front Oncol 2023; 13:1009681. [PMID: 37305563 PMCID: PMC10248729 DOI: 10.3389/fonc.2023.1009681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 05/05/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology. Methods The original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset - MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin-stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique. Results To prioritize the performance of nuclei segmentation, we evaluated the model's outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 - 96.6), 81.8 (95% CI: 80.8 - 83.0), and 69.3 (95% CI: 68.2 - 70.0), respectively, on the internal test data set. Conclusion Our proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis.
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Affiliation(s)
- Rashadul Islam Sumon
- Department of Digital Anti-Aging Healthcare, Ubiquitous-Anti-aging-Healthcare Research Center (u-AHRC), Inje University, Gimhae, Republic of Korea
| | - Subrata Bhattacharjee
- Department of Computer Engineering, Ubiquitous-Anti-aging-Healthcare Research Center (u-AHRC), Inje University, Gimhae, Republic of Korea
| | - Yeong-Byn Hwang
- Department of Digital Anti-Aging Healthcare, Ubiquitous-Anti-aging-Healthcare Research Center (u-AHRC), Inje University, Gimhae, Republic of Korea
| | - Hafizur Rahman
- Department of Digital Anti-Aging Healthcare, Ubiquitous-Anti-aging-Healthcare Research Center (u-AHRC), Inje University, Gimhae, Republic of Korea
| | - Hee-Cheol Kim
- Department of Digital Anti-Aging Healthcare, Ubiquitous-Anti-aging-Healthcare Research Center (u-AHRC), Inje University, Gimhae, Republic of Korea
| | - Wi-Sun Ryu
- Artificial Intelligence R&D Center, JLK Inc., Seoul, Republic of Korea
| | - Dong Min Kim
- Artificial Intelligence R&D Center, JLK Inc., Seoul, Republic of Korea
| | - Nam-Hoon Cho
- Department of Pathology, Yonsei University Hospital, Seoul, Republic of Korea
| | - Heung-Kook Choi
- Department of Computer Engineering, Ubiquitous-Anti-aging-Healthcare Research Center (u-AHRC), Inje University, Gimhae, Republic of Korea
- Artificial Intelligence R&D Center, JLK Inc., Seoul, Republic of Korea
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16
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Ding K, Zhou M, Wang H, Gevaert O, Metaxas D, Zhang S. A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer. Sci Data 2023; 10:231. [PMID: 37085533 PMCID: PMC10121551 DOI: 10.1038/s41597-023-02125-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/31/2023] [Indexed: 04/23/2023] Open
Abstract
The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotation burden. To aid in computational pathology, synthetic data generation, curation, and annotation present a cost-effective means to quickly enable data diversity that is required to boost model performance at different stages. In this study, we introduce a large-scale synthetic pathological image dataset paired with the annotation for nuclei semantic segmentation, termed as Synthetic Nuclei and annOtation Wizard (SNOW). The proposed SNOW is developed via a standardized workflow by applying the off-the-shelf image generator and nuclei annotator. The dataset contains overall 20k image tiles and 1,448,522 annotated nuclei with the CC-BY license. We show that SNOW can be used in both supervised and semi-supervised training scenarios. Extensive results suggest that synthetic-data-trained models are competitive under a variety of model training settings, expanding the scope of better using synthetic images for enhancing downstream data-driven clinical tasks.
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Affiliation(s)
- Kexin Ding
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28262, USA
| | - Mu Zhou
- Sensebrain Research, San Jose, CA, 95131, USA
| | - He Wang
- Department of Pathology, Yale University, New Haven, CT, 06520, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Dimitris Metaxas
- Department of Computer Science, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Shaoting Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
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17
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Rezaeian Shiadeh SN, Hadizadeh F, Khodaverdi E, Gorji Valokola M, Rakhshani S, Kamali H, Nokhodchi A. Injectable In-Situ Forming Depot Based on PLGA and PLGA-PEG-PLGA for Sustained-Release of Risperidone: In Vitro Evaluation and Pharmacokinetics in Rabbits. Pharmaceutics 2023; 15:pharmaceutics15041229. [PMID: 37111714 PMCID: PMC10143068 DOI: 10.3390/pharmaceutics15041229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/24/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
In the current research, novel drug delivery systems based on in situ forming gel (ISFG) (PLGA-PEG-PLGA) and in situ forming implant (ISFI) (PLGA) were developed for one-month risperidone delivery. In vitro release evaluation, pharmacokinetics, and histopathology studies of ISFI, ISFG, and Risperdal CONSTA® were compared in rabbits. Formulation containing 50% (w/w %) of PLGA-PEG-PLGA triblock revealed sustained release for about one month. Scanning electron microscopy (SEM) showed a porous structure for ISFI, while a structure with fewer pores was observed in the triblock. Cell viability in ISFG formulation in the first days was more than ISFI due to the gradual release of NMP to the release medium. Pharmacokinetic data displayed that optimal PLGA-PEG-PLGA creates a consistent serum level in vitro and in vivo through 30 days, and histopathology results revealed nearly slight to moderate pathological signs in the rabbit's organs. The shelf life of the accelerated stability test didn't affect the results of the release rate test and demonstrated stability in 24 months. This research confirms the better potential of the ISFG system compared with ISFI and Risperdal CONSTA®, which would increase patients' compliance and avoid problems of further oral therapy.
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Affiliation(s)
| | - Farzin Hadizadeh
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad 9177948974, Iran
| | - Elham Khodaverdi
- Targeted Drug Delivery Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad 9177948974, Iran
| | - Mahmoud Gorji Valokola
- Department of Pharmacodynamics and Toxicology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad 9177948974, Iran
| | - Saleh Rakhshani
- Department of Pharmaceutics, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad 9177948974, Iran
| | - Hossein Kamali
- Targeted Drug Delivery Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad 9177948974, Iran
| | - Ali Nokhodchi
- Lupin Pharmaceutical Research Center, 4006 NW 124th Ave., Coral Springs, Florida, FL 33065, USA
- Pharmaceutics Research Laboratory, School of Life Sciences, University of Sussex, Brighton BN1 9QJ, UK
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18
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Wang J, Qin L, Chen D, Wang J, Han BW, Zhu Z, Qiao G. An improved Hover-net for nuclear segmentation and classification in histopathology images. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08394-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Verdicchio M, Brancato V, Cavaliere C, Isgrò F, Salvatore M, Aiello M. A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon 2023; 9:e14371. [PMID: 36950640 PMCID: PMC10025040 DOI: 10.1016/j.heliyon.2023.e14371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
Background and objectives The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. Methods A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. Conclusions The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.
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Affiliation(s)
| | - Valentina Brancato
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
- Corresponding author.
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Claudio 21, Naples, 80125, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
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Wang R, Gu Y, Zhang T, Yang J. Fast cancer metastasis location based on dual magnification hard example mining network in whole-slide images. Comput Biol Med 2023; 158:106880. [PMID: 37044050 DOI: 10.1016/j.compbiomed.2023.106880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/28/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Breast cancer has become the most common form of cancer among women. In recent years, deep learning has shown great potential in aiding the diagnosis of pathological images, particularly through the use of convolutional neural networks for locating lymph node metastasis under gigapixel whole slide images (WSIs). However, the massive size of these images at the highest magnification introduces redundant computation during the inference process. Additionally, the diversity of biological textures and structures within WSIs can cause confusion for classifiers, particularly in identifying hard examples. As a result, the trade-off between accuracy and efficiency remains a critical issue for whole-slide image metastasis localization. In this paper, we propose a novel two-stream network that takes a pair of low- and high-magnification image patches as input for identifying hard examples during the training phase. Specifically, our framework focuses on samples where the outputs of the two magnification networks are dissimilar. We adopt a dual magnification hard mining loss to re-weight the ambiguous samples. To more efficiently locate tumor metastasis cells in whole slide images, the two stream networks are decomposed into a cascaded network during the inference phase. The low magnification WSIs scanned by the low-mag network generate a coarse probability map, and the suspicious areas in the map are refined by the high-mag network. Finally, we evaluate our fast location dual magnification hard example mining network on the Camelyon16 breast cancer whole-slide image dataset. Experiments demonstrate that our proposed method achieves a 0.871 FROC score with a faster inference time, and our high magnification network also achieves a 0.88 FROC score.
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Affiliation(s)
- Rui Wang
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China.
| | - Yun Gu
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China.
| | - Tianyi Zhang
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China
| | - Jie Yang
- Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 20040, China.
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Mou T, Liang J, Vu TN, Tian M, Gao Y. A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue. SENSORS (BASEL, SWITZERLAND) 2023; 23:1432. [PMID: 36772473 PMCID: PMC9921444 DOI: 10.3390/s23031432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/15/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.
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Affiliation(s)
- Tian Mou
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China
| | - Jianwen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China
| | - Trung Nghia Vu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE 17177 Stockholm, Sweden
| | - Mu Tian
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China
| | - Yi Gao
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518000, China
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22
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Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data. Diagnostics (Basel) 2023; 13:diagnostics13030481. [PMID: 36766587 PMCID: PMC9914433 DOI: 10.3390/diagnostics13030481] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
The biopsy is a gold standard method for tumor grading. However, due to its invasive nature, it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer-aided diagnosis (CAD) tool is required. Recently, many magnetic resonance imaging (MRI)-based CAD tools have been proposed for brain tumor grading. The MRI has several sequences, which can express tumor structure in different ways. However, a suitable MRI sequence for brain tumor classification is not yet known. The most common brain tumor is 'glioma', which is the most fatal form. Therefore, in the proposed study, to maximize the classification ability between low-grade versus high-grade glioma, three datasets were designed comprising three MRI sequences: T1-Weighted (T1W), T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Further, five well-established convolutional neural networks, AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 were adopted for tumor classification. An ensemble algorithm was proposed using the majority vote of above five deep learning (DL) models to produce more consistent and improved results than any individual model. Five-fold cross validation (K5-CV) protocol was adopted for training and testing. For the proposed ensembled classifier with K5-CV, the highest test accuracies of 98.88 ± 0.63%, 97.98 ± 0.86%, and 94.75 ± 0.61% were achieved for FLAIR, T2W, and T1W-MRI data, respectively. FLAIR-MRI data was found to be most significant for brain tumor classification, where it showed a 4.17% and 0.91% improvement in accuracy against the T1W-MRI and T2W-MRI sequence data, respectively. The proposed ensembled algorithm (MajVot) showed significant improvements in the average accuracy of three datasets of 3.60%, 2.84%, 1.64%, 4.27%, and 1.14%, respectively, against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50.
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23
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Alabdaly AA, El-Sayed WG, Hassan YF. RAMRU-CAM: Residual-Atrous MultiResUnet with Channel Attention Mechanism for cell segmentation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-222631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The task of cell segmentation in microscope images is difficult and popular. In recent years, deep learning-based techniques have made incredible progress in medical and microscopy image segmentation applications. In this paper, we propose a novel deep learning approach called Residual-Atrous MultiResUnet with Channel Attention Mechanism (RAMRU-CAM) for cell segmentation, which combines MultiResUnet architecture with Channel Attention Mechanism (CAM) and Residual-Atrous connections. The Residual-Atrous path mitigates the semantic gap between the encoder and decoder stages and manages the spatial dimension of feature maps. Furthermore, the Channel Attention Mechanism (CAM) blocks are used in the decoder stages to better maintain the spatial details before concatenating the feature maps from the encoder phases to the decoder phases. We evaluated our proposed model on the PhC-C2DH-U373 and Fluo-N2DH-GOWT1 datasets. The experimental results show that our proposed model outperforms recent variants of the U-Net model and the state-of-the-art approaches. We have demonstrated how our model can segment cells precisely while using fewer parameters and low computational complexity.
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Affiliation(s)
- Ammar A. Alabdaly
- Department of Mathematics and Computer Science, Alexandria University, Alexandria, Egypt
| | - Wagdy G. El-Sayed
- Department of Mathematics and Computer Science, Alexandria University, Alexandria, Egypt
| | - Yasser F. Hassan
- Faculty of Computer and Data Science, Alexandria University, Alexandria, Egypt
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24
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Chen H, Durinck S, Patel H, Foreman O, Mesh K, Eastham J, Caothien R, Newman RJ, Roose-Girma M, Darmanis S, Warming S, Lattanzi A, Liang Y, Haley B. Population-wide gene disruption in the murine lung epithelium via AAV-mediated delivery of CRISPR-Cas9 components. MOLECULAR THERAPY - METHODS & CLINICAL DEVELOPMENT 2022; 27:431-449. [DOI: 10.1016/j.omtm.2022.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/27/2022] [Indexed: 11/13/2022]
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25
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Wen T, Tong B, Liu Y, Pan T, Du Y, Chen Y, Zhang S. Review of research on the instance segmentation of cell images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107211. [PMID: 36356384 DOI: 10.1016/j.cmpb.2022.107211] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 10/27/2022] [Accepted: 10/30/2022] [Indexed: 06/16/2023]
Abstract
The instance segmentation of cell images is the basis for conducting cell research and is of great importance for the study and diagnosis of pathologies. To analyze current situations and future developments in the field of cell image instance segmentation, this paper first systematically reviews image segmentation methods based on traditional and deep learning methods. Then, from the three aspects of cell image weak label extraction, cell image instance segmentation, and cell internal structure segmentation, deep-learning-based cell image segmentation methods are analyzed and summarized. Finally, cell image instance segmentation is summarized, and challenges and future developments are discussed.
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Affiliation(s)
- Tingxi Wen
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Binbin Tong
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Yu Liu
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Ting Pan
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Yu Du
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Yuping Chen
- College of Engineering, Huaqiao University, Quanzhou 362021, China.
| | - Shanshan Zhang
- College of Engineering, Huaqiao University, Quanzhou 362021, China.
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Wang Z, Saoud C, Wangsiricharoen S, James AW, Popel AS, Sulam J. Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3952-3968. [PMID: 36037454 PMCID: PMC9825360 DOI: 10.1109/tmi.2022.3202759] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problem where the mislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets.
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27
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Alzoubi I, Bao G, Zheng Y, Wang X, Graeber MB. Artificial intelligence techniques for neuropathological diagnostics and research. Neuropathology 2022. [PMID: 36443935 DOI: 10.1111/neup.12880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 12/03/2022]
Abstract
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.
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Affiliation(s)
- Islam Alzoubi
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Guoqing Bao
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Yuqi Zheng
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
| | - Xiuying Wang
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Manuel B. Graeber
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
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28
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Wu H, Souedet N, Jan C, Clouchoux C, Delzescaux T. A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing. Comput Biol Med 2022; 150:106180. [PMID: 36244305 DOI: 10.1016/j.compbiomed.2022.106180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/21/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases. Code is available at: https://github.com/MIRCen/NeuronInstanceSeg.
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Affiliation(s)
- Huaqian Wu
- CEA-CNRS-UMR 9199, MIRCen, Fontenay-aux-Roses, France
| | | | - Caroline Jan
- CEA-CNRS-UMR 9199, MIRCen, Fontenay-aux-Roses, France
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29
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Andrews M, Yoshida T, Henderson CM, Pflaum H, McGregor A, Lieberman JA, de Boer IH, Vaisar T, Himmelfarb J, Kestenbaum B, Chung JY, Hewitt SM, Santo BA, Ginley B, Sarder P, Rosenberg AZ, Murakami T, Kopp JB, Kuklenyik Z, Hoofnagle AN. Variant APOL1 protein in plasma associates with larger particles in humans and mouse models of kidney injury. PLoS One 2022; 17:e0276649. [PMID: 36279295 PMCID: PMC9591058 DOI: 10.1371/journal.pone.0276649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 10/11/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Genetic variants in apolipoprotein L1 (APOL1), a protein that protects humans from infection with African trypanosomes, explain a substantial proportion of the excess risk of chronic kidney disease affecting individuals with sub-Saharan ancestry. The mechanisms by which risk variants damage kidney cells remain incompletely understood. In preclinical models, APOL1 expressed in podocytes can lead to significant kidney injury. In humans, studies in kidney transplant suggest that the effects of APOL1 variants are predominantly driven by donor genotype. Less attention has been paid to a possible role for circulating APOL1 in kidney injury. METHODS Using liquid chromatography-tandem mass spectrometry, the concentrations of APOL1 were measured in plasma and urine from participants in the Seattle Kidney Study. Asymmetric flow field-flow fractionation was used to evaluate the size of APOL1-containing lipoprotein particles in plasma. Transgenic mice that express wild-type or risk variant APOL1 from an albumin promoter were treated to cause kidney injury and evaluated for renal disease and pathology. RESULTS In human participants, urine concentrations of APOL1 were correlated with plasma concentrations and reduced kidney function. Risk variant APOL1 was enriched in larger particles. In mice, circulating risk variant APOL1-G1 promoted kidney damage and reduced podocyte density without renal expression of APOL1. CONCLUSIONS These results suggest that plasma APOL1 is dynamic and contributes to the progression of kidney disease in humans, which may have implications for treatment of APOL1-associated kidney disease and for kidney transplantation.
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Affiliation(s)
- Michael Andrews
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Teruhiko Yoshida
- Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Clark M. Henderson
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Hannah Pflaum
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Ayako McGregor
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Joshua A. Lieberman
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Ian H. de Boer
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
| | - Tomas Vaisar
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Jonathan Himmelfarb
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
| | - Bryan Kestenbaum
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
| | - Joon-Yong Chung
- Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America
| | - Stephen M. Hewitt
- Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America
| | - Briana A. Santo
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Brandon Ginley
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Avi Z. Rosenberg
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America
| | - Taichi Murakami
- Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Nephrology, Ehime Prefectural Central Hospital, Ehime, Japan
| | - Jeffrey B. Kopp
- Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Zsuzsanna Kuklenyik
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Andrew N. Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
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30
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Hossain MS, Syeed MMM, Fatema K, Hossain MS, Uddin MF. Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:7361. [PMID: 36236459 PMCID: PMC9571354 DOI: 10.3390/s22197361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.
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Affiliation(s)
- Md Shakhawat Hossain
- Department of CS, American International University-Bangladesh, Dhaka 1229, Bangladesh
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
| | - M. M. Mahbubul Syeed
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
- Department of CSE, Independent University, Bangladesh, Dhaka 1229, Bangladesh
| | - Kaniz Fatema
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
- Department of CSE, Independent University, Bangladesh, Dhaka 1229, Bangladesh
| | - Md Sakir Hossain
- Department of CS, American International University-Bangladesh, Dhaka 1229, Bangladesh
| | - Mohammad Faisal Uddin
- RIoT Research Center, Independent University, Bangladesh, Dhaka 1229, Bangladesh
- Department of CSE, Independent University, Bangladesh, Dhaka 1229, Bangladesh
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Teranikar T, Villarreal C, Salehin N, Ijaseun T, Lim J, Dominguez C, Nguyen V, Cao H, Chuong C, Lee J. SCALE SPACE DETECTOR FOR ANALYZING SPATIOTEMPORAL VENTRICULAR CONTRACTILITY AND NUCLEAR MORPHOGENESIS IN ZEBRAFISH. iScience 2022; 25:104876. [PMID: 36034231 PMCID: PMC9404658 DOI: 10.1016/j.isci.2022.104876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 04/01/2022] [Accepted: 07/29/2022] [Indexed: 11/15/2022] Open
Abstract
In vivo quantitative assessment of structural and functional biomarkers is essential for characterizing the pathophysiology of congenital disorders. In this regard, fixed tissue analysis has offered revolutionary insights into the underlying cellular architecture. However, histological analysis faces major drawbacks with respect to lack of spatiotemporal sampling and tissue artifacts during sample preparation. This study demonstrates the potential of light sheet fluorescence microscopy (LSFM) as a non-invasive, 4D (3days + time) optical sectioning tool for revealing cardiac mechano-transduction in zebrafish. Furthermore, we have described the utility of a scale and size-invariant feature detector, for analyzing individual morphology of fused cardiomyocyte nuclei and characterizing zebrafish ventricular contractility. Cardiac defect genes in humans have corresponding zebrafish orthologs Light sheet modality is very effective for non-invasive, 4D modeling of zebrafish Hessian detector is robust to varying nuclei scales and geometric transformations Watershed filter is effective for separating fused cellular volumes
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Affiliation(s)
- Tanveer Teranikar
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Cameron Villarreal
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Nabid Salehin
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Toluwani Ijaseun
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Jessica Lim
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Cynthia Dominguez
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Vivian Nguyen
- Martin High School/ UT Arlington, Arlington, TX, USA
| | - Hung Cao
- Department of Electrical Engineering, UC Irvine, Irvine, CA, USA
| | - Cheng–Jen Chuong
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
| | - Juhyun Lee
- Joint Department of Bioengineering, UT Arlington/UT Southwestern, Arlington, TX, USA
- Department of Medical Education, TCU and UNTHSC School of Medicine, Fort Worth, TX 76107, USA
- Corresponding author
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32
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Qin J, He Y, Zhou Y, Zhao J, Ding B. REU-Net: Region-enhanced nuclei segmentation network. Comput Biol Med 2022; 146:105546. [DOI: 10.1016/j.compbiomed.2022.105546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/24/2022] [Accepted: 04/17/2022] [Indexed: 11/03/2022]
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He W, Liu T, Han Y, Ming W, Du J, Liu Y, Yang Y, Wang L, Jiang Z, Wang Y, Yuan J, Cao C. A review: The detection of cancer cells in histopathology based on machine vision. Comput Biol Med 2022; 146:105636. [PMID: 35751182 DOI: 10.1016/j.compbiomed.2022.105636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/04/2022] [Accepted: 04/28/2022] [Indexed: 12/24/2022]
Abstract
Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time and manpower, and heavily relies on the pathologists' skill and work experience. Therefore, these manual detection approaches are not convenient for the inheritance of domain knowledge, and are not suitable for the rapid development of medical care in the future. The emergence of machine vision can iteratively update and learn the domain knowledge of cancer cell pathology detection to achieve automated, high-precision, and consistent detection. Consequently, this paper reviews the use of machine vision to detect cancer cells in histopathology images, as well as the benefits and drawbacks of various detection approaches. First, we review the application of image preprocessing and image segmentation in histopathology for the detection of cancer cells, and compare the benefits and drawbacks of different algorithms. Secondly, for the characteristics of histopathological cancer cell images, the research progress of shape, color and texture features and other methods is mainly reviewed. Furthermore, for the classification methods of histopathological cancer cell images, the benefits and drawbacks of traditional machine vision approaches and deep learning methods are compared and analyzed. Finally, the above research is discussed and forecasted, with the expected future development tendency serving as a guide for future research.
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Affiliation(s)
- Wenbin He
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Ting Liu
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongjie Han
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China.
| | - Jinguang Du
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yinxia Liu
- Laboratory Medicine of Dongguan Kanghua Hospital, Dongguan, 523808, China
| | - Yuan Yang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
| | - Leijie Wang
- School of Mechanical Engineering, Dongguan University of Technology Dongguan, 523808, China
| | - Zhiwen Jiang
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongqiang Wang
- Zhengzhou Coal Mining Machinery Group Co., Ltd, Zhengzhou, 450016, China
| | - Jie Yuan
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Chen Cao
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China
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34
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Ding R, Prasanna P, Corredor G, Barrera C, Zens P, Lu C, Velu P, Leo P, Beig N, Li H, Toro P, Berezowska S, Baxi V, Balli D, Belete M, Rimm DL, Velcheti V, Schalper K, Madabhushi A. Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome. NPJ Precis Oncol 2022; 6:33. [PMID: 35661148 PMCID: PMC9166700 DOI: 10.1038/s41698-022-00277-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/18/2022] [Indexed: 12/12/2022] Open
Abstract
Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.
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Grants
- UL1 TR002548 NCATS NIH HHS
- R01 CA216579 NCI NIH HHS
- UL1 TR001863 NCATS NIH HHS
- R03 CA219603 NCI NIH HHS
- C06 RR012463 NCRR NIH HHS
- U24 CA199374 NCI NIH HHS
- I01 BX004121 BLRD VA
- R43 EB028736 NIBIB NIH HHS
- U54 CA254566 NCI NIH HHS
- U01 CA239055 NCI NIH HHS
- R37 CA245154 NCI NIH HHS
- R01 CA220581 NCI NIH HHS
- P50 CA196530 NCI NIH HHS
- R01 CA202752 NCI NIH HHS
- R01 CA208236 NCI NIH HHS
- Research reported in this publication was supported by the National Cancer Institute under award numbers 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01, 1U01CA248226-01, 1U54CA254566-01, National Heart, Lung and Blood Institute, 1R01HL15127701A1, National Institute for Biomedical Imaging and Bioengineering 1R43EB028736-01, National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404), the Kidney Precision Medicine Project (KPMP) Glue Grant, the Ohio Third Frontier Technology Validation Fund, the Clinical and Translational Science Collaborative of Cleveland (UL1TR0002548) from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, The Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University, and National Science Foundation Graduate Research Fellowship Program (CON501692).
- A scholarship of the Cancer Research Switzerland (MD-PhD-5088-06-2020).
- the National Cancer Institute under award numbers R03CA219603, R37CA245154, P50CA196530, the Lung Cancer Research Program W81XWH-16-1-0160 and the Stand Up To Cancer – American Cancer Society Lung Cancer Dream Team Translational Research Grants SU2C-AACR-DT1715 and SU2C-AACR-DT22-17
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Affiliation(s)
- Ruiwen Ding
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Germán Corredor
- Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | | | - Philipp Zens
- Institute of Pathology, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Cheng Lu
- Case Western Reserve University, Cleveland, OH, USA
| | - Priya Velu
- Weill Cornell Medical College, New York, NY, USA
| | - Patrick Leo
- Case Western Reserve University, Cleveland, OH, USA
| | - Niha Beig
- Case Western Reserve University, Cleveland, OH, USA
| | - Haojia Li
- Case Western Reserve University, Cleveland, OH, USA
| | - Paula Toro
- Case Western Reserve University, Cleveland, OH, USA
| | - Sabina Berezowska
- Institute of Pathology, University of Bern, Bern, Switzerland
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | | | | | | | | | | | - Anant Madabhushi
- Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
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35
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Graph-Embedded Online Learning for Cell Detection and Tumour Proportion Score Estimation. ELECTRONICS 2022. [DOI: 10.3390/electronics11101642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Cell detection in microscopy images can provide useful clinical information. Most methods based on deep learning for cell detection are fully supervised. Without enough labelled samples, the accuracy of these methods would drop rapidly. To handle limited annotations and massive unlabelled data, semi-supervised learning methods have been developed. However, many of these are trained off-line, and are unable to process new incoming data to meet the needs of clinical diagnosis. Therefore, we propose a novel graph-embedded online learning network (GeoNet) for cell detection. It can locate and classify cells with dot annotations, saving considerable manpower. Trained by both historical data and reliable new samples, the online network can predict nuclear locations for upcoming new images while being optimized. To be more easily adapted to open data, it engages dynamic graph regularization and learns the inherent nonlinear structures of cells. Moreover, GeoNet can be applied to downstream tasks such as quantitative estimation of tumour proportion score (TPS), which is a useful indicator for lung squamous cell carcinoma treatment and prognostics. Experimental results for five large datasets with great variability in cell type and morphology validate the effectiveness and generalizability of the proposed method. For the lung squamous cell carcinoma (LUSC) dataset, the detection F1-scores of GeoNet for negative and positive tumour cells are 0.734 and 0.769, respectively, and the relative error of GeoNet for TPS estimation is 11.1%.
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36
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Ali H, Haq IU, Cui L, Feng J. MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network. BMC Med Inform Decis Mak 2022; 22:90. [PMID: 35379228 PMCID: PMC8978355 DOI: 10.1186/s12911-022-01826-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 03/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background The digital pathology images obtain the essential information about the patient’s disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries. Results Both qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part. Conclusion The MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding.
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Affiliation(s)
- Haider Ali
- School of Information Science and Technology, Northwest University, Xian, China
| | - Imran Ul Haq
- School of Information Science and Technology, Northwest University, Xian, China
| | - Lei Cui
- School of Information Science and Technology, Northwest University, Xian, China.
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xian, China.
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37
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Kiran I, Raza B, Ijaz A, Khan MA. DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images. Comput Biol Med 2022; 143:105267. [PMID: 35114445 DOI: 10.1016/j.compbiomed.2022.105267] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/23/2022] [Accepted: 01/23/2022] [Indexed: 11/16/2022]
Abstract
Cancer is the second deadliest disease globally that can affect any human body organ. Early detection of cancer can increase the chances of survival in humans. Morphometric appearances of histopathology images make it difficult to segment nuclei effectively. We proposed a model to segment overlapped nuclei from H&E stained images. U-Net model achieved state-of-the-art performance in many medical image segmentation tasks; however, we modified the U-Net to learn a distinct set of consistent features. In this paper, we proposed the DenseRes-Unet model by integrating dense blocks in the last layers of the encoder block of U-Net, focused on relevant features from previous layers of the model. Moreover, we take advantage of residual connections with Atrous blocks instead of conventional skip connections, which helps to reduce the semantic gap between encoder and decoder paths. The distance map and binary threshold techniques intensify the nuclei interior and contour information in the images, respectively. The distance map is used to detect the center point of nuclei; moreover, it differentiates among nuclei interior boundary and core area. The distance map lacks a contour problem, which is resolved by using a binary threshold. Binary threshold helps to enhance the pixels around nuclei. Afterward, we fed images into the proposed DenseRes-Unet model, a deep, fully convolutional network to segment nuclei in the images. We have evaluated our model on four publicly available datasets for Nuclei segmentation to validate the model's performance. Our proposed model achieves 89.77% accuracy 90.36% F1-score, and 78.61% Aggregated Jaccard Index (AJI) on Multi organ Nucleus Segmentation (MoNuSeg).
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Affiliation(s)
- Iqra Kiran
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, Pakistan.
| | - Basit Raza
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, Pakistan.
| | - Areesha Ijaz
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, Pakistan.
| | - Muazzam A Khan
- Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
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38
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Su CH, Chung PC, Lin SF, Tsai HW, Yang TL, Su YC. Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22072679. [PMID: 35408293 PMCID: PMC9003085 DOI: 10.3390/s22072679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 05/07/2023]
Abstract
In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the grading and staging of hepatitis according to whether portal areas have fibrous expansion, bridging with other portal areas, or bridging with central veins. Based on these staging criteria, it is necessary to identify portal areas and central veins when performing the Ishak Score staging. The bile ducts have variant types and are very difficult to be detected under a single magnification, hence pathologists must observe bile ducts at different magnifications to obtain sufficient information. This pathologic examinations in routine clinical practice, however, would result in the labor intensive and expensive examination process. Therefore, the automatic quantitative analysis for pathologic examinations has had an increased demand and attracted significant attention recently. A multi-scale inputs of attention convolutional network is proposed in this study to simulate pathologists' examination procedure for observing bile ducts under different magnifications in liver biopsy. The proposed multi-scale attention network integrates cell-level information and adjacent structural feature information for bile duct segmentation. In addition, the attention mechanism of proposed model enables the network to focus the segmentation task on the input of high magnification, reducing the influence from low magnification input, but still helps to provide wider field of surrounding information. In comparison with existing models, including FCN, U-Net, SegNet, DeepLabv3 and DeepLabv3-plus, the experimental results demonstrated that the proposed model improved the segmentation performance on Masson bile duct segmentation task with 72.5% IOU and 84.1% F1-score.
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Affiliation(s)
- Chun-Han Su
- Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan City 701, Taiwan; (C.-H.S.); (P.-C.C.)
| | - Pau-Choo Chung
- Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan City 701, Taiwan; (C.-H.S.); (P.-C.C.)
| | - Sheng-Fung Lin
- Division of Hematology and Oncology, Department of Internal Medicine, E-Da Hospital, Kaohsiung 824, Taiwan;
| | - Hung-Wen Tsai
- Department of Pathology, National Cheng Kung University Hospital, Tainan City 704, Taiwan;
| | - Tsung-Lung Yang
- Kaohsiung Veterans General Hospital, Kaohsiung 813414, Taiwan;
| | - Yu-Chieh Su
- Division of Hematology and Oncology, Department of Internal Medicine, E-Da Hospital, Kaohsiung 824, Taiwan;
- School of Medicine, I-Shou University, Kaohsiung 824, Taiwan
- Correspondence:
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39
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Han W, Cheung AM, Yaffe MJ, Martel AL. Cell segmentation for immunofluorescence multiplexed images using two-stage domain adaptation and weakly labeled data for pre-training. Sci Rep 2022; 12:4399. [PMID: 35292693 PMCID: PMC8924193 DOI: 10.1038/s41598-022-08355-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/07/2022] [Indexed: 12/02/2022] Open
Abstract
Cellular profiling with multiplexed immunofluorescence (MxIF) images can contribute to a more accurate patient stratification for immunotherapy. Accurate cell segmentation of the MxIF images is an essential step. We propose a deep learning pipeline to train a Mask R-CNN model (deep network) for cell segmentation using nuclear (DAPI) and membrane (Na+K+ATPase) stained images. We used two-stage domain adaptation by first using a weakly labeled dataset followed by fine-tuning with a manually annotated dataset. We validated our method against manual annotations on three different datasets. Our method yields comparable results to the multi-observer agreement on an ovarian cancer dataset and improves on state-of-the-art performance on a publicly available dataset of mouse pancreatic tissues. Our proposed method, using a weakly labeled dataset for pre-training, showed superior performance in all of our experiments. When using smaller training sample sizes for fine-tuning, the proposed method provided comparable performance to that obtained using much larger training sample sizes. Our results demonstrate that using two-stage domain adaptation with a weakly labeled dataset can effectively boost system performance, especially when using a small training sample size. We deployed the model as a plug-in to CellProfiler, a widely used software platform for cellular image analysis.
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Affiliation(s)
- Wenchao Han
- Biomarker Imaging Research Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
| | - Alison M Cheung
- Biomarker Imaging Research Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Martin J Yaffe
- Biomarker Imaging Research Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Anne L Martel
- Biomarker Imaging Research Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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40
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41
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Yao K, Sun J, Huang K, Jing L, Liu H, Huang D, Jude C. Analyzing Cell-Scaffold Interaction through Unsupervised 3D Nuclei Segmentation. Int J Bioprint 2022; 8:495. [PMID: 35187282 PMCID: PMC8852265 DOI: 10.18063/ijb.v8i1.495] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/07/2021] [Indexed: 11/23/2022] Open
Abstract
Fibrous scaffolds have been extensively used in three-dimensional (3D) cell culture systems to establish in vitro models in cell biology, tissue engineering, and drug screening. It is a common practice to characterize cell behaviors on such scaffolds using confocal laser scanning microscopy (CLSM). As a noninvasive technology, CLSM images can be utilized to describe cell-scaffold interaction under varied morphological features, biomaterial composition, and internal structure. Unfortunately, such information has not been fully translated and delivered to researchers due to the lack of effective cell segmentation methods. We developed herein an end-to-end model called Aligned Disentangled Generative Adversarial Network (AD-GAN) for 3D unsupervised nuclei segmentation of CLSM images. AD-GAN utilizes representation disentanglement to separate content representation (the underlying nuclei spatial structure) from style representation (the rendering of the structure) and align the disentangled content in the latent space. The CLSM images collected from fibrous scaffold-based culturing A549, 3T3, and HeLa cells were utilized for nuclei segmentation study. Compared with existing commercial methods such as Squassh and CellProfiler, our AD-GAN can effectively and efficiently distinguish nuclei with the preserved shape and location information. Building on such information, we can rapidly screen cell-scaffold interaction in terms of adhesion, migration and proliferation, so as to improve scaffold design.
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Affiliation(s)
- Kai Yao
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, Jiangsu 215123, China.,School of Engineering, University of Liverpool, The Quadrangle, Brownlow Hill, L69 3GH, UK
| | - Jie Sun
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, Jiangsu 215123, China
| | - Kaizhu Huang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, Jiangsu 215123, China
| | - Linzhi Jing
- National University of Singapore (Suzhou) Research Institute, 377 Linquan Street, Suzhou, Jiangsu 215123, China
| | - Hang Liu
- Department of Food Science and Technology, National University of Singapore, 3 Science Drive 2, 117542, Singapore
| | - Dejian Huang
- National University of Singapore (Suzhou) Research Institute, 377 Linquan Street, Suzhou, Jiangsu 215123, China.,Department of Food Science and Technology, National University of Singapore, 3 Science Drive 2, 117542, Singapore
| | - Curran Jude
- School of Engineering, University of Liverpool, The Quadrangle, Brownlow Hill, L69 3GH, UK
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42
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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 2022. [DOI: 10.1007/s10462-021-10121-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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43
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Ray S, Das A, Dhal KG, Gálvez J, Naskar PK. Whale Optimizer-Based Clustering for Breast Histopathology Image Segmentation. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022. [DOI: 10.4018/ijsir.302611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Breast histopathology image segmentation is a complex task due to indiscernibly correlated and noisy regions of interest. Breast histopathological images are composed of different types of cells. Some of these cells can be harmful for humans due to the presence of cancer. Under such circumstances, many segmentation techniques for automatic detection of cancer cells have been proposed considering clustering schemes. However, such clustering methodologies are sensitive to initial cluster centers, which promote false-positive solutions. This paper presents the use of the Whale Optimization Algorithm (WOA) for proper clustering segmentation of breast histopathological images to overcome clustering issues. Also, a rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperforms the other utilized clustering methods in terms of precision, robustness, and quality of the segmented outputs.
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Affiliation(s)
- Swarnajit Ray
- Maulana Abul Kalam Azad University of Technology, India
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44
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Sagiv R, Delgado B, Lavon O, Osipov V, Sade R, Shashar S, Yegodayev KM, Elkabets M, Joshua BZ. Assessment of parathyroid gland cellularity by digital slide analysis. Ann Diagn Pathol 2022; 58:151907. [DOI: 10.1016/j.anndiagpath.2022.151907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 01/01/2022] [Accepted: 01/25/2022] [Indexed: 11/29/2022]
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45
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Whitney J, Dollinger L, Tamrazi B, Hawes D, Couce M, Marcheque J, Judkins A, Margol A, Madabhushi A. Quantitative Nuclear Histomorphometry Predicts Molecular Subtype and Clinical Outcome in Medulloblastomas: Preliminary Findings. J Pathol Inform 2022; 13:100090. [PMID: 36268104 PMCID: PMC9576985 DOI: 10.1016/j.jpi.2022.100090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 12/11/2021] [Indexed: 10/30/2022] Open
Abstract
Molecular subtypes of medulloblastoma [Sonic Hedgehog (SHH), Wingless/INT (WNT), Group 3, and Group 4] are defined by common patterns of gene expression. These differential gene expression patterns appear to result in different histomorphology and prognosis. Quantitative histomorphometry is a well-known method of computer-aided pathology image analysis. The hypotheses we sought to examine in this preliminary proof of concept study were whether computer extracted nuclear morphological features of medulloblastomas from digitized tissue slide images could independently: (1) distinguish between molecularly determined subgroups and (2) identify patterns within these subgroups that correspond with clinical outcome. Our dataset was composed of 46 medulloblastoma patients: 16 SHH (5 dead, 11 survived), 3 WNT (0 dead, 3 survived), 12 Group 3 (4 dead, 8 survived), and 15 were Group 4 (5 dead, 10 survived). A watershed-based thresholding scheme was used to automatically identify individual nuclei within digitized whole slide hematoxylin and eosin tissue images. Quantitative histomorphometric features corresponding to the texture (variation in pixel intensity), shape (variations in size, roundness), and architectural rearrangement (distances between, and number of connected neighbors) of nuclei were subsequently extracted. These features were ranked using feature selection schemes and these top-ranked features were then used to train machine-learning classifiers via threefold cross-validation to separate patients based on: (1) molecular subtype and (2) disease-specific outcomes within the individual molecular subtype groups. SHH and WNT tumors were separated from Groups 3 and 4 tumors with a maximum area under the receiver operating characteristic curve (AUC) of 0.7, survival within Group 3 tumors was predicted with an AUC of 0.92, and Group 3 and 4 patients were separated into high- and low-risk groups with p = 0.002. Model prediction was quantitatively compared with age, stage, and histological subtype using univariate and multivariate Cox hazard ratio models. Age was the most statistically significant variable for predicting survival in Group 3 and 4 tumors, but model predictions had the highest hazard ratio value. Quantitative nuclear histomorphometry can be used to study medulloblastoma genetic expression phenotypes as it may distinguish meaningful features of disease pathology.
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46
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Xie X, Wang X, Liang Y, Yang J, Wu Y, Li L, Sun X, Bing P, He B, Tian G, Shi X. Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review. Front Oncol 2021; 11:763527. [PMID: 34900711 PMCID: PMC8660076 DOI: 10.3389/fonc.2021.763527] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.
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Affiliation(s)
- Xiaoliang Xie
- Department of Colorectal Surgery, General Hospital of Ningxia Medical University, Yinchuan, China.,College of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Xulin Wang
- Department of Oncology Surgery, Central Hospital of Jia Mu Si City, Jia Mu Si, China
| | - Yuebin Liang
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jingya Yang
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, China
| | - Yan Wu
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Li Li
- Beijing Shanghe Jiye Biotech Co., Ltd., Bejing, China
| | - Xin Sun
- Department of Medical Affairs, Central Hospital of Jia Mu Si City, Jia Mu Si, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,IBMC-BGI Center, T`he Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xiaoli Shi
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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47
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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48
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Verma R, Kumar N, Patil A, Kurian NC, Rane S, Graham S, Vu QD, Zwager M, Raza SEA, Rajpoot N, Wu X, Chen H, Huang Y, Wang L, Jung H, Brown GT, Liu Y, Liu S, Jahromi SAF, Khani AA, Montahaei E, Baghshah MS, Behroozi H, Semkin P, Rassadin A, Dutande P, Lodaya R, Baid U, Baheti B, Talbar S, Mahbod A, Ecker R, Ellinger I, Luo Z, Dong B, Xu Z, Yao Y, Lv S, Feng M, Xu K, Zunair H, Hamza AB, Smiley S, Yin TK, Fang QR, Srivastava S, Mahapatra D, Trnavska L, Zhang H, Narayanan PL, Law J, Yuan Y, Tejomay A, Mitkari A, Koka D, Ramachandra V, Kini L, Sethi A. MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3413-3423. [PMID: 34086562 DOI: 10.1109/tmi.2021.3085712] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.
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49
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Rey JS, Li W, Bryer AJ, Beatson H, Lantz C, Engelman AN, Perilla JR. Deep-learning in situ classification of HIV-1 virion morphology. Comput Struct Biotechnol J 2021; 19:5688-5700. [PMID: 34765089 PMCID: PMC8554174 DOI: 10.1016/j.csbj.2021.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 01/04/2023] Open
Abstract
Efficient classifier capable of overcoming inherent problems of small-data training sets. Automated detection and classification of HIV-1 particle morphology from transmission electron micrographs. Three orders of magnitude speed increase in data processing with negligible loss in accuracy.
Transmission electron microscopy (TEM) has a multitude of uses in biomedical imaging due to its ability to discern ultrastructure morphology at the nanometer scale. Through its ability to directly visualize virus particles, TEM has for several decades been an invaluable tool in the virologist’s toolbox. As applied to HIV-1 research, TEM is critical to evaluate activities of inhibitors that block the maturation and morphogenesis steps of the virus lifecycle. However, both the preparation and analysis of TEM micrographs requires time consuming manual labor. Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed a convolutional neural network backbone of a two-stage Region Based Convolutional Neural Network (RCNN) capable of identifying, segmenting and classifying HIV-1 virions at different stages of maturation and morphogenesis. Our results outperformed common RCNN backbones, achieving 80.0% mean Average Precision on a diverse set of micrographs comprising different experimental samples and magnifications. We expect that this tool will be of interest to a broad range of researchers.
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Affiliation(s)
- Juan S Rey
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Wen Li
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Alexander J Bryer
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Hagan Beatson
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Christian Lantz
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Alan N Engelman
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Juan R Perilla
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States.,Data Science Institute, University of Delaware, Newark, DE, United States
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50
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Duanmu H, Wang F, Teodoro G, Kong J. Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints. Bioinformatics 2021; 37:3905-3913. [PMID: 34081103 PMCID: PMC11025700 DOI: 10.1093/bioinformatics/btab418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/07/2021] [Accepted: 06/02/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In most tissue-based biomedical research, the lack of sufficient pathology training images with well-annotated ground truth inevitably limits the performance of deep learning systems. In this study, we propose a convolutional neural network with foveal blur enriching datasets with multiple local nuclei regions of interest derived from original pathology images. We further propose a human-knowledge boosted deep learning system by inclusion to the convolutional neural network new loss function terms capturing shape prior knowledge and imposing smoothness constraints on the predicted probability maps. RESULTS Our proposed system outperforms all state-of-the-art deep learning and non-deep learning methods by Jaccard coefficient, Dice coefficient, Accuracy and Panoptic Quality in three independent datasets. The high segmentation accuracy and execution speed suggest its promising potential for automating histopathology nuclei segmentation in biomedical research and clinical settings. AVAILABILITY AND IMPLEMENTATION The codes, the documentation and example data are available on an open source at: https://github.com/HongyiDuanmu26/FovealBoosted. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongyi Duanmu
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - George Teodoro
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Jun Kong
- Department of Mathematics and Statistics and Computer Science, Georgia State University, Atlanta, GA 30303, USA
- Department of Computer Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
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