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Saxena P, Aggarwal SK, Sinha A, Saxena S, Singh AK. Review of computer-assisted diagnosis model to classify follicular lymphoma histology. Cell Biochem Funct 2024; 42:e4088. [PMID: 38973163 DOI: 10.1002/cbf.4088] [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: 02/22/2024] [Revised: 04/15/2024] [Accepted: 06/28/2024] [Indexed: 07/09/2024]
Abstract
The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.
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Affiliation(s)
- Pranshu Saxena
- School of Computer Science Engineering & Technology, Bennett University, Greater Noida, Uttar Pradesh, India
| | - Sahil Kumar Aggarwal
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Amit Sinha
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Sandeep Saxena
- Greater Noida Institute of Technology, Greater Noida, India
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Seesawad N, Ittichaiwong P, Sudhawiyangkul T, Sawangjai P, Thuwajit P, Boonsakan P, Sripodok S, Veerakanjana K, Charngkaew K, Pongpaibul A, Angkathunyakul N, Hnoohom N, Yuenyong S, Thuwajit C, Wilaiprasitporn T. PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:514-523. [PMID: 39050971 PMCID: PMC11268940 DOI: 10.1109/ojemb.2024.3407351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 05/08/2024] [Accepted: 05/27/2024] [Indexed: 07/27/2024] Open
Abstract
Background: Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined labels for model optimization. Objective: To address this limitation, we propose PseudoCell, an object detection framework for automated centroblast detection in WSI, eliminating the need for extensive pathologist's refined labels. Methods: PseudoCell leverages a combination of pathologist-provided centroblast labels and pseudo-negative labels generated from undersampled false-positive predictions based on cell morphology features. This approach reduces the reliance on time-consuming manual annotations. Results: Our framework significantly reduces the workload for pathologists by accurately identifying and narrowing down areas of interest containing centroblasts. Depending on the confidence threshold, PseudoCell can eliminate 58.18-99.35% of irrelevant tissue areas on WSI, streamlining the diagnostic process. Conclusion: This study presents PseudoCell as a practical and efficient prescreening method for centroblast detection, eliminating the need for refined labels from pathologists. The discussion section provides detailed guidance for implementing PseudoCell in clinical practice.
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Affiliation(s)
- Narongrid Seesawad
- Bio-inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Piyalitt Ittichaiwong
- Siriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Thapanun Sudhawiyangkul
- Bio-inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Phattarapong Sawangjai
- Bio-inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
| | - Peti Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Paisarn Boonsakan
- Department of Pathology, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkok10400Thailand
| | - Supasan Sripodok
- Department of Pathology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Kanyakorn Veerakanjana
- Siriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Komgrid Charngkaew
- Department of Pathology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Ananya Pongpaibul
- Department of Pathology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Napat Angkathunyakul
- Department of Pathology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Narit Hnoohom
- Department of Computer Engineering, Faculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand
| | - Sumeth Yuenyong
- Department of Computer Engineering, Faculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand
| | - Chanitra Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Theerawit Wilaiprasitporn
- Bio-inspired Robotics and Neural Engineering (BRAIN) Lab, School of Information Science and Technology (IST)Vidyasirimedhi Institute of Science & Technology (VISTEC)Rayong21210Thailand
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Yuenyong S, Boonsakan P, Sripodok S, Thuwajit P, Charngkaew K, Pongpaibul A, Angkathunyakul N, Hnoohom N, Thuwajit C. Detection of centroblast cells in H&E stained whole slide image based on object detection. Front Med (Lausanne) 2024; 11:1303982. [PMID: 38384407 PMCID: PMC10879397 DOI: 10.3389/fmed.2024.1303982] [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: 09/28/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction Detection and counting of Centroblast cells (CB) in hematoxylin & eosin (H&E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells and compared with the World Health Organization (WHO) guideline that organizes lymphoma into 3 grades. Spotting and counting CBs is time-consuming and labor intensive. Moreover, there is often disagreement between different readers, and even a single reader may not be able to perform consistently due to many factors. Method We propose an artificial intelligence system that can scan patches from a WSI and detect CBs automatically. The AI system works on the principle of object detection, where the CB is the single class of object of interest. We trained the AI model on 1,669 example instances of CBs that originate from WSI of 5 different patients. The data was split 80%/20% for training and validation respectively. Result The best performance was from YOLOv5x6 model that used the preprocessed CB dataset achieved precision of 0.808, recall of 0.776, mAP at 0.5 IoU of 0.800 and overall mAP of 0.647. Discussion The results show that centroblast cells can be detected in WSI with relatively high precision and recall.
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Affiliation(s)
- Sumeth Yuenyong
- Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Paisarn Boonsakan
- Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Supasan Sripodok
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Peti Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Komgrid Charngkaew
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Ananya Pongpaibul
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Napat Angkathunyakul
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Narit Hnoohom
- Image Information and Intelligence Laboratory, Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Chanitra Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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Saxena P, Goyal A. Accurate demarcation of a biased nucleus from H&E-stained follicular lymphoma tissues samples. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2192550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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Saxena P, Goyal A. Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2162663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Pranshu Saxena
- I.K. Gujral Punjab Technical University, Jalandhar, India
| | - Anjali Goyal
- Department of Computer Applications, GNIMT, Ludhiana, India
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Patnaik V, Mohanty M, Subudhi AK. Identification of healthy biological leafs using hybrid-feature classifier. THE IMAGING SCIENCE JOURNAL 2022. [DOI: 10.1080/13682199.2022.2157533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Vijaya Patnaik
- Department of ECE, ITER, SOA Deemed to be University, Odisha, India
| | - Monalisa Mohanty
- Department of ECE, ITER, SOA Deemed to be University, Odisha, India
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Azevedo Tosta TA, de Faria PR, Neves LA, do Nascimento MZ. Evaluation of statistical and Haralick texture features for lymphoma histological images classification. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2021.1902401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Thaína A. Azevedo Tosta
- Center of Mathematics, Computer Science and Cognition, Federal University of ABC (UFABC), Santo André, Brazil
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), São José dos Campos, Brazil
| | - Paulo R. de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Uberlândia, Brazil
| | - Leandro A. Neves
- Department of Computer Science and Statistics, São Paulo State University (UNESP), São José do Rio Preto, Brazil
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Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders. Pathology 2021; 53:400-407. [PMID: 33642096 DOI: 10.1016/j.pathol.2020.12.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/21/2020] [Indexed: 02/06/2023]
Abstract
Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.
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Maroof N, Khan A, Qureshi SA, Rehman AU, Khalil RK, Shim SO. Mitosis detection in breast cancer histopathology images using hybrid feature space. Photodiagnosis Photodyn Ther 2020; 31:101885. [PMID: 32565178 DOI: 10.1016/j.pdpdt.2020.101885] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/13/2020] [Accepted: 06/12/2020] [Indexed: 12/18/2022]
Abstract
Breast Cancer grading is a challenging task as regards image analysis, which is normally based on mitosis count rate. The mitotic count provides an estimate of aggressiveness of the tumor. The detection of mitosis is a challenging task because in a frame of slides at X40 magnification, there are hundreds of nuclei containing few mitotic nuclei. However, manual counting of mitosis by pathologists is a difficult and time intensive job, moreover conventional method rely mainly on the shape, color, and/or texture features as well as pathologist experience. The objective of this study is to accept the atypaia-2014 mitosis detection challenge, automate the process of mitosis detection and a proposal of a hybrid feature space that provides better discrimination of mitotic and non-mitotic nuclei by combining color features with morphological and texture features. To exploit color channels, they were first selected, and then normalized and cumulative histograms were computed in wavelet domain. A detailed analysis presented on these features in different color channels of respective color spaces using Random Forest (RF) and Support Vector Machine (SVM) classifiers. The proposed hybrid feature space when used with SVM classifier achieved a detection rate of 78.88% and F-measure of 72.07%. Our results, especially high detection rate, indicate that proposed hybrid feature space model contains discriminant information for mitotic nuclei, being therefore a very capable are for exploration to improve the quality of the diagnostic assistance in histopathology.
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Affiliation(s)
- Noorulain Maroof
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
| | - Asifullah Khan
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
| | - Shahzad Ahmad Qureshi
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, 45650, Islamabad, Pakistan
| | - Aziz Ul Rehman
- Agri & Biophotonics Division, National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences (PIEAS) P.O. Nilore, 45650 Islamabad, Pakistan.
| | | | - Seong-O Shim
- Faculty of Computing and IT, University of Jeddah, Jeddah, Saudi Arabia
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El Achi H, Khoury JD. Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology. Cancers (Basel) 2020; 12:cancers12040797. [PMID: 32224980 PMCID: PMC7226574 DOI: 10.3390/cancers12040797] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 03/24/2020] [Indexed: 12/15/2022] Open
Abstract
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.
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Affiliation(s)
- Hanadi El Achi
- Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
| | - Joseph D. Khoury
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
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Yoon JS, Choi EY, Saad M, Choi TS. Automated integrated system for stained neuron detection: An end-to-end framework with a high negative predictive rate. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 180:105028. [PMID: 31437805 DOI: 10.1016/j.cmpb.2019.105028] [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: 07/25/2018] [Revised: 07/13/2019] [Accepted: 08/09/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Mapping the architecture of the brain is essential for identifying the neural computations that affect behavior. Traditionally in histology, stained objects in tissue slices are hand-marked under a microscope in a manually intensive, time-consuming process. An integrated hardware and software system is needed to automate image acquisition, image processing, and object detection. Such a system would enable high throughput tissue analysis to rapidly map an entire brain. METHODS We demonstrate an automated system to detect neurons using a monkey brain slice immunohistochemically stained for retrogradely labeled neurons. The proposed system obtains a reconstructed image of the sample, and stained neurons are detected in three steps. First, the reconstructed image is pre-processed using adaptive histogram equalization. Second, candidates for stained neurons are segmented from each region via marker-controlled watershed transformation (MCWT) using maximally stable extremal regions (MSERs). Third, the candidates are categorized as neurons or non-neurons using deep transfer learning via pre-trained convolutional neural networks (CNN). RESULTS The proposed MCWT algorithm was compared qualitatively against MorphLibJ and an IHC analysis tool, while our unified classification algorithm was evaluated quantitatively using ROC analyses. The proposed classification system was first compared with five previously developed layers (AlexNet, VGG-16, VGG-19, GoogleNet, and ResNet). A comparison with conventional multi-stage frameworks followed using six off-the-shelf classifiers [Bayesian network (BN), support vector machines (SVM), decision tree (DT), bagging (BAG), AdaBoost (ADA), and logistic regression (LR)] and two descriptors (LBP and HOG). The system achieved a 0.918 F1-score with an 86.6% negative prediction value. Remarkably, other metrics such as precision, recall, and F-scores surpassed the 90% threshold compared to traditional methods. CONCLUSIONS We demonstrate a fully automated, integrated hardware and software system for rapidly acquiring focused images and identifying neurons from a stained brain slice. This system could be adapted for the identification of stained features of any biological tissue.
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Affiliation(s)
- Ji-Seok Yoon
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 61005, Republic of Korea
| | - Eun Young Choi
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Maliazurina Saad
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 61005, Republic of Korea
| | - Tae-Sun Choi
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 61005, Republic of Korea.
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Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol 2019; 20:e253-e261. [PMID: 31044723 PMCID: PMC8711251 DOI: 10.1016/s1470-2045(19)30154-8] [Citation(s) in RCA: 497] [Impact Index Per Article: 99.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 02/28/2019] [Accepted: 03/13/2019] [Indexed: 02/06/2023]
Abstract
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
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Affiliation(s)
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Metin N Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Niazi MKK, Senaras C, Pennell M, Arole V, Tozbikian G, Gurcan MN. Relationship between the Ki67 index and its area based approximation in breast cancer. BMC Cancer 2018; 18:867. [PMID: 30176814 PMCID: PMC6122570 DOI: 10.1186/s12885-018-4735-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 08/08/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The Ki67 Index has been extensively studied as a prognostic biomarker in breast cancer. However, its clinical adoption is largely hampered by the lack of a standardized method to assess Ki67 that limits inter-laboratory reproducibility. It is important to standardize the computation of the Ki67 Index before it can be effectively used in clincial practice. METHOD In this study, we develop a systematic approach towards standardization of the Ki67 Index. We first create the ground truth consisting of tumor positive and tumor negative nuclei by registering adjacent breast tissue sections stained with Ki67 and H&E. The registration is followed by segmentation of positive and negative nuclei within tumor regions from Ki67 images. The true Ki67 Index is then approximated with a linear model of the area of positive to the total area of tumor nuclei. RESULTS When tested on 75 images of Ki67 stained breast cancer biopsies, the proposed method resulted in an average root mean square error of 3.34. In comparison, an expert pathologist resulted in an average root mean square error of 9.98 and an existing automated approach produced an average root mean square error of 5.64. CONCLUSIONS We show that it is possible to approximate the true Ki67 Index accurately without detecting individual nuclei and also statically demonstrate the weaknesses of commonly adopted approaches that use both tumor and non-tumor regions together while compensating for the latter with higher order approximations.
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Affiliation(s)
| | - Caglar Senaras
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, USA
| | - Michael Pennell
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, USA
| | - Vidya Arole
- Department of Biomedical Informatics, The Ohio State University, Columbus, USA
| | - Gary Tozbikian
- Department of Pathology, The Ohio State University, Columbus, USA
| | - Metin N. Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, USA
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Azevedo Tosta TA, Faria PR, Batista VR, Neves LA, do Nascimento MZ. Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.11.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Jordan J, Goldstein JS, Jaye DL, Gurcan M, Flowers CR, Cooper LAD. Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies. JCO Clin Cancer Inform 2018; 2. [PMID: 30637363 DOI: 10.1200/cci.17.00039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Purpose Lymphoid malignancies are remarkably heterogeneous, with variations in outcomes and clinical, biologic, and histologic presentation complicating classification according to the World Health Organization guidelines. Incorrect classification of lymphoid neoplasms can result in suboptimal therapeutic strategies for individual patients and confound the interpretation of clinical trials involving personalized, class-based treatments. This review discusses the potential role of pathology informatics in improving the classification accuracy and objectivity for lymphoid malignancies. Design We identified peer-reviewed publications examining pathology informatics approaches for the classification of lymphoid malignancies, reviewed developments in the lymphoma classification systems, and summarized computational methods for pathologic assessment that can impact practice. Results Computer-assisted pathology image analysis algorithms in lymphoma most commonly have been applied to follicular lymphoma to address biologic heterogeneity and subjectivity in the process of classification. Conclusion Objective methods are available to assist pathologists in lymphoma classification and grading, and have been demonstrated to provide measurable benefits in specific contexts. Future validation and extension of these approaches will require datasets that link high resolution pathology images available for image analysis algorithms with clinical variables and follow up outcomes.
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Affiliation(s)
- Jacob Jordan
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Jordan S Goldstein
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - David L Jaye
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Metin Gurcan
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Christopher R Flowers
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Lee A D Cooper
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
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Öztürk Ş, Akdemir B. Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procs.2018.05.057] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Fatima K, Majeed H, Irshad H. Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology. Microsc Res Tech 2017; 80:851-861. [PMID: 28379628 DOI: 10.1002/jemt.22874] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Accepted: 03/17/2017] [Indexed: 11/11/2022]
Abstract
Meningioma subtypes classification is a real-world multiclass problem from the realm of neuropathology. The major challenge in solving this problem is the inherent complexity due to high intra-class variability and low inter-class variation in tissue samples. The development of computational methods to assist pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this article, we proposed an optimized evolutionary framework for the classification of benign meningioma into four subtypes. This framework investigates the imperative role of RGB color channels for discrimination of tumor subtypes and compute structural, statistical and spectral phenotypes. An evolutionary technique, Genetic Algorithm, in combination with Support Vector Machine is applied to tune classifier parameters and to select the best possible combination of extracted phenotypes that improved the classification accuracy (94.88%) on meningioma histology dataset, provided by the Institute of Neuropathology, Bielefeld. These statistics show that computational framework can robustly discriminate four subtypes of benign meningioma and may aid pathologists in the diagnosis and classification of these lesions.
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Affiliation(s)
- Kiran Fatima
- Department of Computer Science, National University of Computer and Emerging Sciences, A. K. Brohi Road, H-11/4, Islamabad, Pakistan
| | - Hammad Majeed
- Department of Computer Science, National University of Computer and Emerging Sciences, A. K. Brohi Road, H-11/4, Islamabad, Pakistan
| | - Humayun Irshad
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
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Segmentation methods of H&E-stained histological images of lymphoma: A review. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.05.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
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Classification of Nuclei in Follicular Lyphoma Tissue Sections Using Different Stains and Bayesian Networks. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-32703-7_47] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Saito A, Numata Y, Hamada T, Horisawa T, Cosatto E, Graf HP, Kuroda M, Yamamoto Y. A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix. J Pathol Inform 2016; 7:36. [PMID: 27688927 PMCID: PMC5027740 DOI: 10.4103/2153-3539.189699] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 07/25/2016] [Indexed: 01/14/2023] Open
Abstract
Background: Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. Methods and Results: In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. Conclusion: CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.
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Affiliation(s)
- Akira Saito
- Department of Quantitative Pathology and Immunology, Tokyo Medical University, Tokyo, Japan; Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan
| | | | | | | | - Eric Cosatto
- Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA
| | - Hans-Peter Graf
- Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA
| | - Masahiko Kuroda
- Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan
| | - Yoichiro Yamamoto
- Department of Pathology, Shinshu University School of Medicine, Nagano, Japan
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Angel Arul Jothi J, Mary Anita Rajam V. Effective segmentation of orphan annie-eye nuclei from papillary thyroid carcinoma histopathology images using a probabilistic model and region-based active contour. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2016.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Zhao M, Wu A, Song J, Sun X, Dong N. Automatic screening of cervical cells using block image processing. Biomed Eng Online 2016; 15:14. [PMID: 26847685 PMCID: PMC4743397 DOI: 10.1186/s12938-016-0131-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 01/26/2016] [Indexed: 11/28/2022] Open
Abstract
Background Cervical cancer is the second leading cause of female-specific cancer-related deaths after breast cancer, especially in developing countries. However, the incidence of the disease may be significantly decreased if the patient is diagnosed in the pre-cancerous lesion stage or earlier. In recent years, computer-based algorithms are widely used in cervical cancer screening. Most of the proposed algorithms follow the procedure of segmentation, feature extraction, and then classification. Nevertheless, few of the existing segmentation methods are as flexible and robust as the human visual system, and the complexity of the algorithms makes it difficult for clinical application. Methods In this study, a computer-assisted analytical approach is proposed to identify the existence of suspicious cells in a whole slide cervical cell image (WSCCI). The main difference between our method and the conventional algorithm is that the image is divided into blocks with certain size instead of segmented cells, which can greatly reduce the computational complexity. Via data analysis, some texture and color histogram features show significant differences between blocks with and without suspicious cells. Therefore these features can be used as the input of the support vector machine classifier. 1100 non-background blocks (110 suspicious blocks) are trained to build a model, while 1040 blocks (491 non-background blocks) from 12 other WSCCIs are tested to verify the feasibility of the algorithm. Results The experimental results show that the accuracy of our method is about 98.98 %. More importantly, the sensitivity, which is more fatal in cancer screening, is 95.0 % according to the images tested in the study, while the specificity is 99.33 %. Conclusion The analysis of the algorithm is based on block images, which is different from conventional methods. Although some analysis work should be done in advance, the later processing speed will be greatly enhanced with the establishment of the model. Furthermore, since the algorithm is based on the actual WSCCI, the method will be of directive significance for clinical screening.
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Affiliation(s)
- Meng Zhao
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
| | - Aiguo Wu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
| | - Jingjing Song
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China.
| | - Xuguo Sun
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China.
| | - Na Dong
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
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Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 2016; 9:234-63. [PMID: 26742143 PMCID: PMC5233461 DOI: 10.1109/rbme.2016.2515127] [Citation(s) in RCA: 212] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
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Fauzi MFA, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, Gru A, Kurt H, Losos M, Joehlin-Price A, Kavran C, Smith SM, Nowacki N, Mansor S, Lozanski G, Gurcan MN. Classification of follicular lymphoma: the effect of computer aid on pathologists grading. BMC Med Inform Decis Mak 2015; 15:115. [PMID: 26715518 PMCID: PMC4696238 DOI: 10.1186/s12911-015-0235-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 12/15/2015] [Indexed: 11/28/2022] Open
Abstract
Background Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias. Methods In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured. Results FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates “acceptable” diagnostic performance. Conclusions The results of this study show that FLAGS can be useful in increasing the pathologists’ accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists’ grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability. Electronic supplementary material The online version of this article (doi:10.1186/s12911-015-0235-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Michael Pennell
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Berkman Sahiner
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Weijie Chen
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Arwa Shana'ah
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Jessica Hemminger
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Alejandro Gru
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Habibe Kurt
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Michael Losos
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Amy Joehlin-Price
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Christina Kavran
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Stephen M Smith
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Nicholas Nowacki
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Sharmeen Mansor
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Gerard Lozanski
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Metin N Gurcan
- Department of Biomedical Informatics, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
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Barker J, Hoogi A, Depeursinge A, Rubin DL. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med Image Anal 2015; 30:60-71. [PMID: 26854941 DOI: 10.1016/j.media.2015.12.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 12/02/2015] [Accepted: 12/07/2015] [Indexed: 02/07/2023]
Abstract
Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.
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Affiliation(s)
- Jocelyn Barker
- Department of Medicine (Stanford Biomedical Informatics Research), Stanford University School of Medicine, CA, USA.
| | - Assaf Hoogi
- Department of Radiology, Stanford University School of Medicine, CA, USA.
| | - Adrien Depeursinge
- Department of Radiology, Stanford University School of Medicine, CA, USA; Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, CA, USA; Department of Medicine (Stanford Biomedical Informatics Research), Stanford University School of Medicine, CA, USA.
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Hameed KS, Banumathi A, Ulaganathan G. Performance evaluation of maximal separation techniques in immunohistochemical scoring of tissue images. Micron 2015; 79:29-35. [DOI: 10.1016/j.micron.2015.07.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Revised: 07/28/2015] [Accepted: 07/28/2015] [Indexed: 10/23/2022]
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Michail E, Dimitropoulos K, Koletsa T, Kostopoulos I, Grammalidis N. Morphological and textural analysis of centroblasts in low-thickness sliced tissue biopsies of follicular lymphoma. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:3374-7. [PMID: 25570714 DOI: 10.1109/embc.2014.6944346] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents a new method for discriminating centroblast (CB) from non-centroblast cells in microscopic images acquired from tissue biopsies of follicular lymphoma. In the proposed method tissue sections are sliced at a low thickness level, around 1-1.5 μm, which provides a more detailed depiction of the nuclei and other textural information of cells usually not distinguishable in thicker specimens, such as 4-5 μm, that have been used in the past by other researchers. To identify CBs, a morphological and textural analysis is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. The generated feature vector is then used as input in a two-class SVM classifier with ε-Support Vector Regression and radial basis kernel function. Experimental results with an annotated dataset consisting of 300 images of centroblasts and non-centroblasts, derived from high-power field images of follicular lymphoma stained with Hematoxylin and Eosin, have shown the great potential of the proposed method with an average detection rate of 97.44%.
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Fauzi MFA, Gokozan HN, Elder B, Puduvalli VK, Pierson CR, Otero JJ, Gurcan MN. A multi-resolution textural approach to diagnostic neuropathology reporting. J Neurooncol 2015; 124:393-402. [PMID: 26255070 PMCID: PMC4782607 DOI: 10.1007/s11060-015-1872-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 07/27/2015] [Indexed: 10/23/2022]
Abstract
We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.
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Affiliation(s)
| | | | - Brad Elder
- Department of Neurological Surgery, The Ohio State University, Columbus, OH, USA
| | - Vinay K. Puduvalli
- Division of Neuro-oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Christopher R. Pierson
- Department of Pathology, The Ohio State University, Columbus, OH, USA
- Department of Pathology and Laboratory Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
- Division of Anatomy, The Ohio State University, Columbus, OH, USA
| | - José Javier Otero
- Department of Pathology, The Ohio State University, Columbus, OH, USA
- Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, 4169 Graves Hall, 333 W 10th Avenue, Columbus, OH 43210, USA
| | - Metin N. Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH 43210, USA
- 320-K Lincoln Tower, Columbus, USA
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Automatic detection and morphological delineation of bacteriophages in electron microscopy images. Comput Biol Med 2015; 64:101-16. [PMID: 26164031 DOI: 10.1016/j.compbiomed.2015.06.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Revised: 06/06/2015] [Accepted: 06/18/2015] [Indexed: 11/22/2022]
Abstract
Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host.
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Segmentation of immunohistochemical staining of β-catenin expression of oral cancer using EM algorithm. J Taibah Univ Med Sci 2015. [DOI: 10.1016/j.jtumed.2014.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Irshad H, Veillard A, Roux L, Racoceanu D. Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev Biomed Eng 2014; 7:97-114. [PMID: 24802905 DOI: 10.1109/rbme.2013.2295804] [Citation(s) in RCA: 278] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.
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Cao G, Li L, Chen W, Yu Y, Shi J, Zhang G, Liu X. Effective identification and localization of immature precursors in bone marrow biopsy. Med Biol Eng Comput 2014; 53:215-26. [DOI: 10.1007/s11517-014-1223-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2013] [Accepted: 10/23/2014] [Indexed: 10/24/2022]
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Fatima K, Arooj A, Majeed H. A new texture and shape based technique for improving meningioma classification. Microsc Res Tech 2014; 77:862-73. [DOI: 10.1002/jemt.22409] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 06/25/2014] [Accepted: 07/13/2014] [Indexed: 11/11/2022]
Affiliation(s)
- Kiran Fatima
- Department of Computer Science; National University of Computer and Emerging Sciences; A. K. Brohi Road H-11/4 Islamabad Pakistan
| | - Arshia Arooj
- Department of Computer Science; National University of Computer and Emerging Sciences; A. K. Brohi Road H-11/4 Islamabad Pakistan
| | - Hammad Majeed
- Department of Computer Science; National University of Computer and Emerging Sciences; A. K. Brohi Road H-11/4 Islamabad Pakistan
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Guo Y, Xu X, Wang Y, Wang Y, Xia S, Yang Z. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images. Microsc Res Tech 2014; 77:547-59. [PMID: 24777764 DOI: 10.1002/jemt.22373] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 03/21/2014] [Accepted: 04/15/2014] [Indexed: 02/01/2023]
Abstract
Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images.
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Affiliation(s)
- Yanen Guo
- Key laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
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Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma. Pediatr Res 2014; 75:302-14. [PMID: 24216542 DOI: 10.1038/pr.2013.217] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Accepted: 06/09/2013] [Indexed: 11/08/2022]
Abstract
BACKGROUND Risk classification and treatment stratification for cancer patients is restricted by our incomplete picture of the complex and unknown interactions between the patient's organism and tumor tissues (transformed cells supported by tumor stroma). Moreover, all clinical factors and laboratory studies used to indicate treatment effectiveness and outcomes are by their nature a simplification of the biological system of cancer, and cannot yet incorporate all possible prognostic indicators. METHODS A multiparametric analysis on 184 tumor cylinders was performed. To highlight the benefit of integrating digitized medical imaging into this field, we present the results of computational studies carried out on quantitative measurements, taken from stromal and cancer cells and various extracellular matrix fibers interpenetrated by glycosaminoglycans, and eight current approaches to risk stratification systems in patients with primary and nonprimary neuroblastoma. RESULTS New tumor tissue indicators from both fields, the cellular and the extracellular elements, emerge as reliable prognostic markers for risk stratification and could be used as molecular targets of specific therapies. CONCLUSION The key to dealing with personalized therapy lies in the mathematical modeling. The use of bioinformatics in patient-tumor-microenvironment data management allows a predictive model in neuroblastoma.
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Kornaropoulos EN, Niazi MKK, Lozanski G, Gurcan MN. Histopathological image analysis for centroblasts classification through dimensionality reduction approaches. Cytometry A 2013; 85:242-55. [PMID: 24376080 DOI: 10.1002/cyto.a.22432] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Revised: 11/30/2013] [Accepted: 12/03/2013] [Indexed: 11/10/2022]
Abstract
We present two novel automated image analysis methods to differentiate centroblast (CB) cells from noncentroblast (non-CB) cells in digital images of H&E-stained tissues of follicular lymphoma. CB cells are often confused by similar looking cells within the tissue, therefore a system to help their classification is necessary. Our methods extract the discriminatory features of cells by approximating the intrinsic dimensionality from the subspace spanned by CB and non-CB cells. In the first method, discriminatory features are approximated with the help of singular value decomposition (SVD), whereas in the second method they are extracted using Laplacian Eigenmaps. Five hundred high-power field images were extracted from 17 slides, which are then used to compose a database of 213 CB and 234 non-CB region of interest images. The recall, precision, and overall accuracy rates of the developed methods were measured and compared with existing classification methods. Moreover, the reproducibility of both classification methods was also examined. The average values of the overall accuracy were 99.22% ± 0.75% and 99.07% ± 1.53% for COB and CLEM, respectively. The experimental results demonstrate that both proposed methods provide better classification accuracy of CB/non-CB in comparison with the state of the art methods.
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Affiliation(s)
- Evgenios N Kornaropoulos
- Informatics and Telematics Institute-Centre for Research and Technology Hellas (ITI-CERTH), Thessaloniki, Greece
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Lozanski G, Pennell M, Shana'ah A, Zhao W, Gewirtz A, Racke F, Hsi E, Simpson S, Mosse C, Alam S, Swierczynski S, Hasserjian RP, Gurcan MN. Inter-reader variability in follicular lymphoma grading: Conventional and digital reading. J Pathol Inform 2013; 4:30. [PMID: 24392244 PMCID: PMC3869955 DOI: 10.4103/2153-3539.120747] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 09/03/2013] [Indexed: 11/04/2022] Open
Abstract
CONTEXT Pathologists grade follicular lymphoma (FL) cases by selecting 10, random high power fields (HPFs), counting the number of centroblasts (CBs) in these HPFs under the microscope and then calculating the average CB count for the whole slide. Previous studies have demonstrated that there is high inter-reader variability among pathologists using this methodology in grading. AIMS The objective of this study was to explore if newly available digital reading technologies can reduce inter-reader variability. SETTINGS AND DESIGN IN THIS STUDY, WE CONSIDERED THREE DIFFERENT READING CONDITIONS (RCS) IN GRADING FL: (1) Conventional (glass-slide based) to establish the baseline, (2) digital whole slide viewing, (3) digital whole slide viewing with selected HPFs. Six board-certified pathologists from five different institutions read 17 FL slides in these three different RCs. RESULTS Although there was relative poor consensus in conventional reading, with lack of consensus in 41.2% of cases, which was similar to previously reported studies; we found that digital reading with pre-selected fields improved the inter-reader agreement, with only 5.9% lacking consensus among pathologists. CONCLUSIONS Digital whole slide RC resulted in the worst concordance among pathologists while digital whole slide reading selected HPFs improved the concordance. Further studies are underway to determine if this performance can be sustained with a larger dataset and our automated HPF and CB detection algorithms can be employed to further improve the concordance.
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Affiliation(s)
- Gerard Lozanski
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Michael Pennell
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Arwa Shana'ah
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Weiqiang Zhao
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Amy Gewirtz
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Frederick Racke
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Eric Hsi
- Cleveland Clinic, Cleveland, OH, USA
| | - Sabrina Simpson
- Department of Pathology, Central Ohio Pathology Associates, Westerville, OH, USA
| | | | - Shadia Alam
- Department of Pathology, Battle Creek, MI, USA
| | | | | | - Metin N Gurcan
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, USA
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Samols MA, Smith NE, Gerber JM, Vuica-Ross M, Gocke CD, Burns KH, Borowitz MJ, Cornish TC, Duffield AS. Software-automated counting of Ki-67 proliferation index correlates with pathologic grade and disease progression of follicular lymphomas. Am J Clin Pathol 2013; 140:579-87. [PMID: 24045557 DOI: 10.1309/ajcptma1f6lwytqv] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
OBJECTIVES To examine the accuracy of software-assisted measurement of the Ki-67 proliferation index (PI) and its correlation with the grade and clinical progression of follicular lymphoma (FL). METHODS High-power field equivalents were extracted from H&E- and Ki-67-immunostained slides of FL, and a nuclear quantitation algorithm was used to calculate a PI. Representative fields were manually counted for validation with close agreement. RESULTS The PI was significantly higher in World Health Organization grade 3 FL than grade 1 to 2 FL. Disease progression, as defined by subsequent treatment with radiation or cytotoxic chemotherapy, was also significantly associated with elevated PI but not pathologic grade. CONCLUSIONS These data show that software-automated quantitation of Ki-67 can provide both a useful adjunct to pathologic grade in FL and improved prognostic information for patients.
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Affiliation(s)
- Mark A. Samols
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Nathan E. Smith
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jonathan M. Gerber
- Division of Hematology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Milena Vuica-Ross
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Christopher D. Gocke
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kathleen H. Burns
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Michael J. Borowitz
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Toby C. Cornish
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Amy S. Duffield
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
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40
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Bueno G, Fernández-Carrobles MM, Déniz O, Salido J, Vállez N, García-Rojo M. An entropy-based automated approach to prostate biopsy ROI segmentation. Diagn Pathol 2013. [PMCID: PMC3849514 DOI: 10.1186/1746-1596-8-s1-s24] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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41
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Veta M, van Diest PJ, Kornegoor R, Huisman A, Viergever MA, Pluim JPW. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS One 2013; 8:e70221. [PMID: 23922958 PMCID: PMC3726421 DOI: 10.1371/journal.pone.0070221] [Citation(s) in RCA: 172] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Accepted: 06/17/2013] [Indexed: 12/11/2022] Open
Abstract
The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.
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Affiliation(s)
- Mitko Veta
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
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42
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Song JW, Lee JH. New morphological features for grading pancreatic ductal adenocarcinomas. BIOMED RESEARCH INTERNATIONAL 2013; 2013:175271. [PMID: 23984321 PMCID: PMC3741920 DOI: 10.1155/2013/175271] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 04/07/2013] [Accepted: 04/24/2013] [Indexed: 01/05/2023]
Abstract
Pathological diagnosis is influenced by subjective factors such as the individual experience and knowledge of doctors. Therefore, it may be interpreted in different ways for the same symptoms. The appearance of digital pathology has created good foundation for objective diagnoses based on quantitative feature analysis. Recently, numerous studies are being done to develop automated diagnosis based on the digital pathology. But there are as of yet no general automated methods for pathological diagnosis due to its specific nature. Therefore, specific methods according to a type of disease and a lesion could be designed. This study proposes quantitative features that are designed to diagnose pancreatic ductal adenocarcinomas. In the diagnosis of pancreatic ductal adenocarcinomas, the region of interest is a duct that consists of lumen and epithelium. Therefore, we first segment the lumen and epithelial nuclei from a tissue image. Then, we extract the specific features to diagnose the pancreatic ductal adenocarcinoma from the segmented objects. The experiment evaluated the classification performance of the SVM learned by the proposed features. The results showed an accuracy of 94.38% in the experiment distinguishing between pancreatic ductal adenocarcinomas and normal tissue and a classification accuracy of 77.03% distinguishing between the stages of pancreatic ductal adenocarcinomas.
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Affiliation(s)
- Jae-Won Song
- Department of Computer & Information Engineering, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402-751, Republic of Korea
| | - Ju-Hong Lee
- Department of Computer & Information Engineering, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402-751, Republic of Korea
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43
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Arslan S, Ersahin T, Cetin-Atalay R, Gunduz-Demir C. Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1121-1131. [PMID: 23549886 DOI: 10.1109/tmi.2013.2255309] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms.
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Affiliation(s)
- Salim Arslan
- Department of Computer Engineering, Bilkent University, TR-06800 Ankara, Turkey.
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44
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Bueno G, Déniz O, Salido J, Milagro Fernández M, Vállez N, García-Rojo M. Colour Model Analysis for Histopathology Image Processing. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/978-94-007-5389-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
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45
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Song JW, Lee JH, Choi JH, Chun SJ. Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features. Comput Biol Med 2012. [PMID: 23200461 DOI: 10.1016/j.compbiomed.2012.10.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Generally, pathological diagnosis using an electron microscope is time-consuming and likely to result in a subjective judgment, because pathologists perform manual screening of tissue slides at high magnifications. Recently, the advent of digital pathology technology has provided the basis for convenient screening and quantitative analysis by digitizing tissue slides through a computer system. However, a screening process with high magnification still takes quite a long time. To solve these problems, recently the use of computer-aided design techniques for performing pathologic diagnosis has been increasing in digital pathology. For pathological diagnosis, we need different diagnostic methods for different regions with different characteristics. Therefore, in order to effectively diagnose different lesions and types of diseases, a quantitative method for extracting specific features is required in computerized pathologic diagnosis. This study is about an automated differential diagnosis system to differentiate between benign serous cystadenoma and possibly-malignant mucinous cystadenoma. In order to diagnose cystic tumors, the first step is identifying a cystic region and inspecting its epithelial cells. First, we identify the lumen boundary of a cyst using the Direction Cumulative Map considering 8-ways. Then, the Epithelial Nuclei Identification algorithm is used to discern epithelial nuclei. After that, three morphological features for the differential diagnosis of mucinous and serous cystadenomas are extracted. To demonstrate the superiority of the proposed features, the experiments compared performance of the classifiers learned by using the proposed morphological features and the classical morphological features based on nuclei. The classifiers in the simulations are as follows; Bayesian Classifier, k-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. The results show that all classifiers using the proposed features have the best classification performance.
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Affiliation(s)
- Jae-Won Song
- Department of Computer & Information Engineering, Inha University, 253, Yonghyun-dong, Incheon 402 751, Republic of Korea.
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46
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Bueno G, González R, Déniz O, García-Rojo M, González-García J, Fernández-Carrobles MM, Vállez N, Salido J. A parallel solution for high resolution histological image analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 108:388-401. [PMID: 22522064 DOI: 10.1016/j.cmpb.2012.03.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2011] [Revised: 02/25/2012] [Accepted: 03/17/2012] [Indexed: 05/31/2023]
Abstract
This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.
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Affiliation(s)
- G Bueno
- VISILAB, E.T.S. Ingenieros Industriales, Universidad de Castilla-La Mancha, Spain.
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47
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Cooper LAD, Carter AB, Farris AB, Wang F, Kong J, Gutman DA, Widener P, Pan TC, Cholleti SR, Sharma A, Kurc TM, Brat DJ, Saltz JH. Digital Pathology: Data-Intensive Frontier in Medical Imaging: Health-information sharing, specifically of digital pathology, is the subject of this paper which discusses how sharing the rich images in pathology can stretch the capabilities of all otherwise well-practiced disciplines. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2012; 100:991-1003. [PMID: 25328166 PMCID: PMC4197933 DOI: 10.1109/jproc.2011.2182074] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.
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Affiliation(s)
- Lee A. D. Cooper
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Alexis B. Carter
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA
| | - Alton B. Farris
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA
| | - Fusheng Wang
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Jun Kong
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - David A. Gutman
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Patrick Widener
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Tony C. Pan
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Sharath R. Cholleti
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Ashish Sharma
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Tahsin M. Kurc
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
| | - Daniel J. Brat
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA
| | - Joel H. Saltz
- Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA
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48
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Cooper LAD, Kong J, Gutman DA, Wang F, Gao J, Appin C, Cholleti S, Pan T, Sharma A, Scarpace L, Mikkelsen T, Kurc T, Moreno CS, Brat DJ, Saltz JH. Integrated morphologic analysis for the identification and characterization of disease subtypes. J Am Med Inform Assoc 2012; 19:317-23. [PMID: 22278382 PMCID: PMC3277636 DOI: 10.1136/amiajnl-2011-000700] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2011] [Accepted: 12/26/2011] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Morphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events. MATERIALS AND METHODS Methodology was applied to 162 glioblastomas from The Cancer Genome Atlas to identify morphology-driven clusters and their clinical and molecular correlates. Signatures of patient-specific tumor morphology were generated from analysis of 200 million cells in 462 whole slide images. Morphology-driven clusters were interrogated for associations with patient outcome, response to therapy, molecular classifications, and genetic alterations. An additional layer of deep, genome-wide analysis identified characteristic transcriptional, epigenetic, and copy number variation events. RESULTS AND DISCUSSION Analysis of glioblastoma identified three prognostically significant patient clusters (median survival 15.3, 10.7, and 13.0 months, log rank p=1.4e-3). Clustering results were validated in a separate dataset. Clusters were characterized by molecular events in nuclear compartment signaling including developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.
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Affiliation(s)
- Lee A D Cooper
- Center for Comprehensive Informatics, Emory University, Atlanta, Georgia 30322, USA.
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Kong H, Gurcan M, Belkacem-Boussaid K. Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:1661-77. [PMID: 21486712 PMCID: PMC3165069 DOI: 10.1109/tmi.2011.2141674] [Citation(s) in RCA: 101] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the segmentation part, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC color space can achieve most discrimination in terms of classification (segmentation) performance. To speed up the texture feature extraction process, we develop an efficient LFT extraction algorithm based on image shifting and image integral. For the splitting part, given a connected component of the segmentation map, we initially differentiate whether it is a touching-cell clump or a single nontouching cell. The differentiation is mainly based on the distance between the most likely radial-symmetry center and the geometrical center of the connected component. The boundaries of touching-cell clumps are smoothed out by Fourier shape descriptor before carrying out an iterative, concave-point and radial-symmetry based splitting algorithm. To test the validity, effectiveness and efficiency of the framework, it is applied to follicular lymphoma pathological images, which exhibit complex background and extracellular texture with nonuniform illumination condition. For comparison purposes, the results of the proposed segmentation algorithm are evaluated against the outputs of superpixel, graph-cut, mean-shift, and two state-of-the-art pathological image segmentation methods using ground-truth that was established by manual segmentation of cells in the original images. Our segmentation algorithm achieves better results than the other compared methods. The results of splitting are evaluated in terms of under-splitting, over-splitting, and encroachment errors. By summing up the three types of errors, we achieve a total error rate of 5.25% per image.
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Affiliation(s)
- Hui Kong
- Department of Biomedical Informatics, the Ohio State University, Columbus, OH, USA,
| | - Metin Gurcan
- Department of Biomedical Informatics, the Ohio State University, Columbus, OH, USA,
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