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Parwani AV, Patel A, Zhou M, Cheville JC, Tizhoosh H, Humphrey P, Reuter VE, True LD. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS). J Pathol Inform 2023; 14:100177. [PMID: 36654741 PMCID: PMC9841212 DOI: 10.1016/j.jpi.2022.100177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.
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Affiliation(s)
- Anil V. Parwani
- The Ohio State University, Columbus, Ohio, USA
- Corresponding author.
| | - Ankush Patel
- The Ohio State University, 2441 60th Ave SE, Mercer Island, Washington 98040, USA
| | - Ming Zhou
- Tufts University, Medford, Massachusetts, USA
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2
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Patel AU, Mohanty SK, Parwani AV. Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics. Surg Pathol Clin 2022; 15:759-785. [PMID: 36344188 DOI: 10.1016/j.path.2022.08.001] [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] [Indexed: 06/16/2023]
Abstract
As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.
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Affiliation(s)
- Ankush Uresh Patel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Sambit K Mohanty
- Surgical and Molecular Pathology, Advanced Medical Research Institute, Plot No. 1, Near Jayadev Vatika Park, Khandagiri, Bhubaneswar, Odisha 751019. https://twitter.com/SAMBITKMohanty1
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Cooperative Human Tissue Network (CHTN) Midwestern Division Polaris Innovation Centre, 2001 Polaris Parkway Suite 1000, Columbus, OH 43240, USA.
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3
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Ramamurthy K, Varikuti AR, Gupta B, Aswani N. A deep learning network for Gleason grading of prostate biopsies using EfficientNet. BIOMED ENG-BIOMED TE 2022; 68:187-198. [PMID: 36332194 DOI: 10.1515/bmt-2022-0201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
Abstract
Objectives
The most crucial part in the diagnosis of cancer is severity grading. Gleason’s score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.
Methods
In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.
Result
To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.
Conclusions
The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.
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Affiliation(s)
- Karthik Ramamurthy
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology , Chennai , India
| | - Abinash Reddy Varikuti
- School of Computer Science Engineering, Vellore Institute of Technology , Chennai , India
| | - Bhavya Gupta
- School of Computer Science Engineering, Vellore Institute of Technology , Chennai , India
| | - Nehal Aswani
- School of Electronics Engineering, Vellore Institute of Technology , Chennai , India
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4
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Abstract
PURPOSE OF REVIEW Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology. RECENT FINDINGS There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application. SUMMARY In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.
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Abstract
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.
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Ali T, Masood K, Irfan M, Draz U, Nagra AA, Asif M, Alshehri BM, Glowacz A, Tadeusiewicz R, Mahnashi MH, Yasin S. Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis. ENTROPY 2020; 22:e22121370. [PMID: 33279915 PMCID: PMC7761953 DOI: 10.3390/e22121370] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 11/24/2020] [Accepted: 12/01/2020] [Indexed: 12/12/2022]
Abstract
In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.
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Affiliation(s)
- Tariq Ali
- Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan;
| | - Khalid Masood
- Department of Computer Science, Lahore Garrison University, Lahore 54792, Pakistan; (K.M.); (A.A.N.); (M.A.)
| | - Muhammad Irfan
- Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
- Correspondence: (M.I.); (U.D.); (A.G.)
| | - Umar Draz
- Department of Computer Science, University of Sahiwal, Sahiwal, Punjab 57000, Pakistan
- Correspondence: (M.I.); (U.D.); (A.G.)
| | - Arfan Ali Nagra
- Department of Computer Science, Lahore Garrison University, Lahore 54792, Pakistan; (K.M.); (A.A.N.); (M.A.)
| | - Muhammad Asif
- Department of Computer Science, Lahore Garrison University, Lahore 54792, Pakistan; (K.M.); (A.A.N.); (M.A.)
| | - Bandar M. Alshehri
- Department of Clinical Laboratory, Faculty of Applied Medical Sciences, Najran University, P.O. Box 1988, Najran 61441, Saudi Arabia;
| | - Adam Glowacz
- Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland
- Correspondence: (M.I.); (U.D.); (A.G.)
| | - Ryszard Tadeusiewicz
- Department of Biocybernetics and Biomedical Engineering, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland;
| | - Mater H. Mahnashi
- Department of Medicinal Chemistry, Pharmacy School, Najran University, Najran 61441, Saudi Arabia;
| | - Sana Yasin
- Department of Computer Science, University of Okara, Okara 56130, Pakistan;
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Deng S, Zhang X, Yan W, Chang EIC, Fan Y, Lai M, Xu Y. Deep learning in digital pathology image analysis: a survey. Front Med 2020; 14:470-487. [PMID: 32728875 DOI: 10.1007/s11684-020-0782-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 03/05/2020] [Indexed: 12/21/2022]
Abstract
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.
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Affiliation(s)
- Shujian Deng
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | - Xin Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | - Wen Yan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | | | - Yubo Fan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China
| | - Maode Lai
- Department of Pathology, School of Medicine, Zhejiang University, Hangzhou, 310007, China
| | - Yan Xu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
- Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China.
- Microsoft Research Asia, Beijing, 100080, China.
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8
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Han W, Johnson C, Warner A, Gaed M, Gomez JA, Moussa M, Chin J, Pautler S, Bauman G, Ward AD. Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens. J Med Imaging (Bellingham) 2020; 7:047501. [PMID: 32715024 DOI: 10.1117/1.jmi.7.4.047501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 07/06/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs. Approach: We proposed a calibration-free adaptive thresholding algorithm, which compensates for staining variability and yields consistent tissue component maps (TCMs) of the nuclei, lumina, and other tissues. We used and compared three machine learning methods for classifying each cancer versus noncancer region of interest (ROI) throughout each WSI: (1) conventional machine learning methods and 14 texture features extracted from TCMs, (2) transfer learning with pretrained AlexNet fine-tuned by TCM ROIs, and (3) transfer learning with pretrained AlexNet fine-tuned with raw image ROIs. Results: The three methods yielded areas under the receiver operating characteristic curve of 0.96, 0.98, and 0.98, respectively, in leave-one-patient-out cross validation using 1.3 million ROIs from 286 mid-gland whole-mount WSIs from 68 patients. Conclusion: Transfer learning with the use of TCMs demonstrated state-of-the-art overall performance and is more stable with respect to sample size across different tissue types. For the tissue types involving Gleason 5 (most aggressive) cancer, it achieved the best performance compared to the other tested methods. This tool can be translated to clinical workflow to assist graphical and quantitative pathology reporting for surgical specimens upon further multicenter validation.
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Affiliation(s)
- Wenchao Han
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Canada.,Lawson Health Research Institute, London, Ontario, Canada.,Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Carol Johnson
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Canada
| | - Andrew Warner
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Canada
| | - Mena Gaed
- Western University, Department of Pathology and Laboratory Medicine, London, Ontario, Canada
| | - Jose A Gomez
- Western University, Department of Pathology and Laboratory Medicine, London, Ontario, Canada
| | - Madeleine Moussa
- Western University, Department of Pathology and Laboratory Medicine, London, Ontario, Canada
| | - Joseph Chin
- Western University, Department of Oncology, London, Ontario, Canada.,Western University, Department of Surgery, London, Ontario, Canada
| | - Stephen Pautler
- Western University, Department of Oncology, London, Ontario, Canada.,Western University, Department of Surgery, London, Ontario, Canada
| | - Glenn Bauman
- Lawson Health Research Institute, London, Ontario, Canada.,Western University, Department of Medical Biophysics, London, Ontario, Canada.,Western University, Department of Oncology, London, Ontario, Canada
| | - Aaron D Ward
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Canada.,Lawson Health Research Institute, London, Ontario, Canada.,Western University, Department of Medical Biophysics, London, Ontario, Canada.,Western University, Department of Oncology, London, Ontario, Canada
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9
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Han W, Johnson C, Gaed M, Gómez JA, Moussa M, Chin JL, Pautler S, Bauman GS, Ward AD. Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens. Sci Rep 2020; 10:9911. [PMID: 32555410 PMCID: PMC7303108 DOI: 10.1038/s41598-020-66849-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 05/19/2020] [Indexed: 11/10/2022] Open
Abstract
Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.
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Affiliation(s)
- Wenchao Han
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Ontario, Canada. .,Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada. .,Lawson Health Research Institute, London, Ontario, Canada.
| | - Carol Johnson
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Ontario, Canada.,Lawson Health Research Institute, London, Ontario, Canada
| | - Mena Gaed
- Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada
| | - José A Gómez
- Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada
| | - Madeleine Moussa
- Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada
| | - Joseph L Chin
- Department of Surgery, University of Western Ontario, London, Ontario, Canada.,Department of Oncology, University of Western Ontario, London, Ontario, Canada
| | - Stephen Pautler
- Department of Surgery, University of Western Ontario, London, Ontario, Canada.,Department of Oncology, University of Western Ontario, London, Ontario, Canada
| | - Glenn S Bauman
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.,Department of Oncology, University of Western Ontario, London, Ontario, Canada.,Lawson Health Research Institute, London, Ontario, Canada
| | - Aaron D Ward
- Baines Imaging Research Laboratory, London Regional Cancer Program, London, Ontario, Canada. .,Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada. .,Department of Oncology, University of Western Ontario, London, Ontario, Canada. .,Lawson Health Research Institute, London, Ontario, Canada.
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10
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Karimi D, Nir G, Fazli L, Black PC, Goldenberg L, Salcudean SE. Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation. IEEE J Biomed Health Inform 2020; 24:1413-1426. [DOI: 10.1109/jbhi.2019.2944643] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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11
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Ji MY, Yuan L, Lu SM, Gao MT, Zeng Z, Zhan N, Ding YJ, Liu ZR, Huang PX, Lu C, Dong WG. Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study. J Transl Med 2020; 18:129. [PMID: 32178690 PMCID: PMC7077008 DOI: 10.1186/s12967-020-02297-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 03/11/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. METHODS 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. RESULTS The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. CONCLUSION Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.
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Affiliation(s)
- Meng-Yao Ji
- Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China.,Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Lei Yuan
- Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei, China. .,Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China.
| | - Shi-Min Lu
- Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China.,Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Meng-Ting Gao
- Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Zhi Zeng
- Department of Pathology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Na Zhan
- Department of Pathology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Yi-Juan Ding
- Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Zheng-Ru Liu
- Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China
| | - Ping-Xiao Huang
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cheng Lu
- College of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi, China.
| | - Wei-Guo Dong
- Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China.
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12
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A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10010338] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.
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13
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García G, Colomer A, Naranjo V. First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning. ENTROPY (BASEL, SWITZERLAND) 2019; 21:E356. [PMID: 33267070 PMCID: PMC7514840 DOI: 10.3390/e21040356] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 03/25/2019] [Accepted: 03/29/2019] [Indexed: 12/14/2022]
Abstract
Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of 0.876 ± 0.026 in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands.
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Affiliation(s)
- Gabriel García
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46008 Valencia, Spain
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14
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Nir G, Karimi D, Goldenberg SL, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G, Thompson DJS, Black PC, Salcudean SE. Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images. JAMA Netw Open 2019; 2:e190442. [PMID: 30848813 PMCID: PMC6484626 DOI: 10.1001/jamanetworkopen.2019.0442] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
IMPORTANCE Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. OBJECTIVES To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. DESIGN, SETTING, AND PARTICIPANTS This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. MAIN OUTCOMES AND MEASURES The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. RESULTS On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60. CONCLUSIONS AND RELEVANCE Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.
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Affiliation(s)
- Guy Nir
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Davood Karimi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - S. Larry Goldenberg
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ladan Fazli
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Brian F. Skinnider
- Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver, British Columbia, Canada
- British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - Peyman Tavassoli
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
- Richmond Hospital, Vancouver Coastal Health, Richmond, British Columbia, Canada
| | - Dmitry Turbin
- Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver, British Columbia, Canada
| | | | - Gang Wang
- British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - Darby J. S. Thompson
- Emmes Canada, Burnaby, British Columbia, Canada
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Peter C. Black
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Septimiu E. Salcudean
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
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15
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Viswanath SE, Chirra PV, Yim MC, Rofsky NM, Purysko AS, Rosen MA, Bloch BN, Madabhushi A. Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Med Imaging 2019; 19:22. [PMID: 30819131 PMCID: PMC6396464 DOI: 10.1186/s12880-019-0308-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 01/10/2019] [Indexed: 11/10/2022] Open
Abstract
Background For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Methods Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. Results The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. Conclusions Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.
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Affiliation(s)
- Satish E Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Prathyush V Chirra
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Michael C Yim
- College of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Neil M Rofsky
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Mark A Rosen
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - B Nicolas Bloch
- Department of Radiology, Boston University School of Medicine, Boston, MA, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
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16
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Hipp JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J, Rodriguez-Canales J, Stumpe MC, Riedlinger G, Rosenberg AZ, Hanson JC, Kunju LP, Emmert-Buck MR, Balis UJ, Tangrea MA. Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools. J Pathol Inform 2018; 9:45. [PMID: 30622835 PMCID: PMC6298131 DOI: 10.4103/jpi.jpi_60_18] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 10/16/2018] [Indexed: 01/05/2023] Open
Abstract
Introduction The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated. Materials and Methods To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process. Results We describe several "use cases" that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms. Conclusions The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.
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Affiliation(s)
- Jason D Hipp
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Google Inc., Mountain View, CA, USA
| | - Donald J Johann
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Yun Chen
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | | | - Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Jaime Rodriguez-Canales
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Medimmune, LLC, Gaithersburg, MD, USA
| | | | - Greg Riedlinger
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Division of Translational Pathology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Avi Z Rosenberg
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jeffrey C Hanson
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA
| | - Lakshmi P Kunju
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael R Emmert-Buck
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Avoneaux Medical Institute, LLC, Baltimore, MD, USA
| | - Ulysses J Balis
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael A Tangrea
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.,Alvin and Lois Lapidus Cancer Institute, Sinai Hospital of Baltimore, LifeBridge Health, Baltimore, MD, USA
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17
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Penzias G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M, Haynes AM, Ponsky L, Fu P, Tiwari P, Viswanath S, Madabhushi A. Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One 2018; 13:e0200730. [PMID: 30169514 PMCID: PMC6118356 DOI: 10.1371/journal.pone.0200730] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 07/02/2018] [Indexed: 12/29/2022] Open
Abstract
Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.
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Affiliation(s)
- Gregory Penzias
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Asha Singanamalli
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Robin Elliott
- University Hospitals, Cleveland, OH, United States of America
| | - Jay Gollamudi
- University Hospitals, Cleveland, OH, United States of America
| | - Natalie Shih
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Michael Feldman
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - Warick Delprado
- Douglass Hanly Moir Pathology, Macquarie Park, NSW, Australia
| | - Sarita Tiwari
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Maret Böhm
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Anne-Maree Haynes
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Lee Ponsky
- University Hospitals, Cleveland, OH, United States of America
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Satish Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
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18
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Ren J, Sadimin ET, Wang D, Epstein JI, Foran DJ, Qi X. Computer aided analysis of prostate histopathology images Gleason grading especially for Gleason score 7. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:3013-6. [PMID: 26736926 DOI: 10.1109/embc.2015.7319026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Clinically, prostate adenocarcinoma is diagnosed by recognizing certain morphology on histology. While the Gleason grading system has been shown to be the strongest prognostic factor for men with prostrate adenocarcinoma, there is a significant intra and interobserver variability between pathologists in assigning this grading system. In this study, we present a new method for prostate gland segmentation from which we then utilize to develop a computer aided Gleason grading. The novelty of our method is a region-based nuclei segmentation to get individual gland without using lumen as prior information. Because each gland region is surrounded by nuclei, individual gland can be segmented by using the structure features and Delaunay Triangulation. The precision, recal and F1 of this approach are 0.94±0.11, 0.60±0.23 and 0.70±0.19 respectively. Our method achieves a high accuracy for prostate gland segmentation with less computation time.
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19
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Banwari A, Sengar N, Dutta MK. Image Processing Based Colorectal Cancer Detection in Histopathological Images. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2018. [DOI: 10.4018/ijehmc.2018040101] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The article proposes an image processing-based automatic methodology for early diagnosis of colorectal cancer. In pathology, staining and sectioning of tissues are routinely used as a primary technique to detect cancer. In this methodology, the colorectal gland tissues are segmented by using adaptive threshold method. Also, it includes an analysis of geometrical features of colorectal tissues as well as it does classification of cancerous cells which classify the cancerous and non-cancerous cell efficiently. The classification is based on discriminatory geometrical features which gives good result. Unlike existing methods, it quantifies lumen and epithelial cells only in the ROI, which makes this method computationally efficient. Automatic supervised classification is accomplished on the extracted discriminatory features using support vector machine classifier. The proposed methodology segments and classifies the cancerous / non-cancerous region with an accuracy of 93.74%. The proposed method is also computationally fast which makes it suitable for real time applications.
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20
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Kainz P, Pfeiffer M, Urschler M. Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization. PeerJ 2017; 5:e3874. [PMID: 29018612 PMCID: PMC5629961 DOI: 10.7717/peerj.3874] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 09/09/2017] [Indexed: 12/17/2022] Open
Abstract
Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.
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Affiliation(s)
- Philipp Kainz
- Institute of Biophysics, Center for Physiological Medicine, Medical University of Graz, Graz, Austria
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Michael Pfeiffer
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Martin Urschler
- Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria
- Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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21
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Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies. Sci Rep 2017; 7:12375. [PMID: 28959011 PMCID: PMC5620056 DOI: 10.1038/s41598-017-12569-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 08/22/2017] [Indexed: 01/03/2023] Open
Abstract
There has been recent substantial interest in extracting sub-visual features from medical images for improved disease characterization compared to what might be achievable via visual inspection alone. Features such as Haralick and Gabor can provide a multi-scale representation of the original image by extracting measurements across differently sized neighborhoods. While these multi-scale features are effective, on large-scale digital pathological images, the process of extracting these features is computationally expensive. Moreover for different problems, different scales and neighborhood sizes may be more or less important and thus a large number of features extracted might end up being redundant. In this paper, we present a Discriminative Scale learning (DiScrn) approach that attempts to automatically identify the distinctive scales at which features are able to best separate cancerous from non-cancerous regions on both radiologic and digital pathology tissue images. To evaluate the efficacy of our approach, our approach was employed to detect presence and extent of prostate cancer on a total of 60 MRI and digitized histopathology images. Compared to a multi-scale feature analysis approach invoking features across all scales, DiScrn achieved 66% computational efficiency while also achieving comparable or even better classifier performance.
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22
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Tennill TA, Gross ME, Frieboes HB. Automated analysis of co-localized protein expression in histologic sections of prostate cancer. PLoS One 2017; 12:e0178362. [PMID: 28552967 PMCID: PMC5446169 DOI: 10.1371/journal.pone.0178362] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 05/11/2017] [Indexed: 12/13/2022] Open
Abstract
An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer.
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Affiliation(s)
- Thomas A. Tennill
- Department of Bioengineering, University of Louisville, Louisville, KY, United States of America
| | - Mitchell E. Gross
- Lawrence J. Elliston Institute for Transformational Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, United States of America
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, United States of America
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23
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Kwak JT, Hewitt SM. Multiview boosting digital pathology analysis of prostate cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:91-99. [PMID: 28325451 PMCID: PMC8171579 DOI: 10.1016/j.cmpb.2017.02.023] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 02/04/2017] [Accepted: 02/15/2017] [Indexed: 05/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Various digital pathology tools have been developed to aid in analyzing tissues and improving cancer pathology. The multi-resolution nature of cancer pathology, however, has not been fully analyzed and utilized. Here, we develop an automated, cooperative, and multi-resolution method for improving prostate cancer diagnosis. METHODS Digitized tissue specimen images are obtained from 5 tissue microarrays (TMAs). The TMAs include 70 benign and 135 cancer samples (TMA1), 74 benign and 89 cancer samples (TMA2), 70 benign and 115 cancer samples (TMA3), 79 benign and 82 cancer samples (TMA4), and 72 benign and 86 cancer samples (TMA5). The tissue specimen images are segmented using intensity- and texture-based features. Using the segmentation results, a number of morphological features from lumens and epithelial nuclei are computed to characterize tissues at different resolutions. Applying a multiview boosting algorithm, tissue characteristics, obtained from differing resolutions, are cooperatively combined to achieve accurate cancer detection. RESULTS In segmenting prostate tissues, the multiview boosting method achieved≥ 0.97 AUC using TMA1. For detecting cancers, the multiview boosting method achieved an AUC of 0.98 (95% CI: 0.97-0.99) as trained on TMA2 and tested on TMA3, TMA4, and TMA5. The proposed method was superior to single-view approaches, utilizing features from a single resolution or merging features from all the resolutions. Moreover, the performance of the proposed method was insensitive to the choice of the training dataset. Trained on TMA3, TMA4, and TMA5, the proposed method obtained an AUC of 0.97 (95% CI: 0.96-0.98), 0.98 (95% CI: 0.96-0.99), and 0.97 (95% CI: 0.96-0.98), respectively. CONCLUSIONS The multiview boosting method is capable of integrating information from multiple resolutions in an effective and efficient fashion and identifying cancers with high accuracy. The multiview boosting method holds a great potential for improving digital pathology tools and research.
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Affiliation(s)
- Jin Tae Kwak
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea.
| | - Stephen M Hewitt
- Tissue Array Research Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, MD 20852, USA
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24
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Singh M, Kalaw EM, Giron DM, Chong KT, Tan CL, Lee HK. Gland segmentation in prostate histopathological images. J Med Imaging (Bellingham) 2017; 4:027501. [PMID: 28653016 PMCID: PMC5479152 DOI: 10.1117/1.jmi.4.2.027501] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 06/01/2017] [Indexed: 01/02/2023] Open
Abstract
Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.
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Affiliation(s)
- Malay Singh
- National University of Singapore, School of Computing, Department of Computer Science, Singapore
- Bioinformatics Institute, Imaging Informatics Division, Matrix, Singapore
| | | | | | - Kian-Tai Chong
- Tan Tock Seng Hospital, Department of Urology, Novena, Singapore
| | - Chew Lim Tan
- National University of Singapore, School of Computing, Department of Computer Science, Singapore
| | - Hwee Kuan Lee
- National University of Singapore, School of Computing, Department of Computer Science, Singapore
- Bioinformatics Institute, Imaging Informatics Division, Matrix, Singapore
- Institute for Infocomm Research, Image and Pervasive Access Lab, Connexis, Singapore
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25
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Leo P, Lee G, Shih NNC, Elliott R, Feldman MD, Madabhushi A. Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images. J Med Imaging (Bellingham) 2016; 3:047502. [PMID: 27803941 DOI: 10.1117/1.jmi.3.4.047502] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Accepted: 09/16/2016] [Indexed: 01/04/2023] Open
Abstract
Quantitative histomorphometry (QH) is the process of computerized feature extraction from digitized tissue slide images to predict disease presence, behavior, and outcome. Feature stability between sites may be compromised by laboratory-specific variables including dye batch, slice thickness, and the whole slide scanner used. We present two new measures, preparation-induced instability score and latent instability score, to quantify feature instability across and within datasets. In a use case involving prostate cancer, we examined QH features which may detect cancer on whole slide images. Using our method, we found that five feature families (graph, shape, co-occurring gland tensor, sub-graph, and texture) were different between datasets in 19.7% to 48.6% of comparisons while the values expected without site variation were 4.2% to 4.6%. Color normalizing all images to a template did not reduce instability. Scanning the same 34 slides on three scanners demonstrated that Haralick features were most substantively affected by scanner variation, being unstable in 62% of comparisons. We found that unstable feature families performed significantly worse in inter- than intrasite classification. Our results appear to suggest QH features should be evaluated across sites to assess robustness, and class discriminability alone should not represent the benchmark for digital pathology feature selection.
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Affiliation(s)
- Patrick Leo
- Case Western Reserve University , Department of Biomedical Engineering, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States
| | - George Lee
- Case Western Reserve University , Department of Biomedical Engineering, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States
| | - Natalie N C Shih
- University of Pennsylvania , Department of Pathology, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States
| | - Robin Elliott
- Case Western Reserve University , Department of Pathology, 11100 Euclid Avenue, Cleveland, Ohio 44106, United States
| | - Michael D Feldman
- University of Pennsylvania , Department of Pathology, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States
| | - Anant Madabhushi
- Case Western Reserve University , Department of Biomedical Engineering, 2071 Martin Luther King Jr. Drive, Cleveland, Ohio 44106, United States
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BenTaieb A, Nosrati MS, Li-Chang H, Huntsman D, Hamarneh G. Clinically-inspired automatic classification of ovarian carcinoma subtypes. J Pathol Inform 2016; 7:28. [PMID: 27563487 PMCID: PMC4977973 DOI: 10.4103/2153-3539.186899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 04/12/2016] [Indexed: 11/04/2022] Open
Abstract
CONTEXT It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists' workflow, we propose an automatic framework for ovarian carcinoma classification. MATERIALS AND METHODS Our method is inspired by pathologists' workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. RESULTS This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier's confusion (confusion matrix) between the five different ovarian carcinoma subtypes agrees with clinician's confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. CONCLUSIONS Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician's diagnostic procedure by providing a second opinion.
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Affiliation(s)
- Aïcha BenTaieb
- Department of Computing Sciences, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada
| | - Masoud S Nosrati
- Department of Computing Sciences, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada
| | - Hector Li-Chang
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David Huntsman
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ghassan Hamarneh
- Department of Computing Sciences, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada
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Sarnecki JS, Burns KH, Wood LD, Waters KM, Hruban RH, Wirtz D, Wu PH. A robust nonlinear tissue-component discrimination method for computational pathology. J Transl Med 2016; 96:450-8. [PMID: 26779829 PMCID: PMC4808351 DOI: 10.1038/labinvest.2015.162] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 11/05/2015] [Accepted: 11/07/2015] [Indexed: 02/01/2023] Open
Abstract
Advances in digital pathology, specifically imaging instrumentation and data management, have allowed for the development of computational pathology tools with the potential for better, faster, and cheaper diagnosis, prognosis, and prediction of disease. Images of tissue sections frequently vary in color appearance across research laboratories and medical facilities because of differences in tissue fixation, staining protocols, and imaging instrumentation, leading to difficulty in the development of robust computational tools. To address this challenge, we propose a novel nonlinear tissue-component discrimination (NLTD) method to register automatically the color space of histopathology images and visualize individual tissue components, independent of color differences between images. Our results show that the NLTD method could effectively discriminate different tissue components from different types of tissues prepared at different institutions. Further, we demonstrate that NLTD can improve the accuracy of nuclear detection and segmentation algorithms, compared with using conventional color deconvolution methods, and can quantitatively analyze immunohistochemistry images. Together, the NLTD method is objective, robust, and effective, and can be easily implemented in the emerging field of computational pathology.
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Affiliation(s)
- Jacob S. Sarnecki
- Johns Hopkins Physical Sciences - Oncology Center, The Johns Hopkins University, Baltimore, Maryland 21218, USA, Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Kathleen H. Burns
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21231, USA, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Laura D. Wood
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21231, USA, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA
| | - Kevin M. Waters
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21231, USA
| | - Ralph H. Hruban
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21231, USA, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA
| | - Denis Wirtz
- Johns Hopkins Physical Sciences - Oncology Center, The Johns Hopkins University, Baltimore, Maryland 21218, USA, Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, USA, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA,Co-corresponding authors: Denis Wirtz () and Pei-Hsun Wu ()
| | - Pei-Hsun Wu
- Johns Hopkins Physical Sciences - Oncology Center, The Johns Hopkins University, Baltimore, Maryland 21218, USA, Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland 21218, USA,Co-corresponding authors: Denis Wirtz () and Pei-Hsun Wu ()
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28
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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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Fernández-Carrobles MM, Bueno G, Déniz O, Salido J, García-Rojo M, González-López L. Influence of Texture and Colour in Breast TMA Classification. PLoS One 2015; 10:e0141556. [PMID: 26513238 PMCID: PMC4626403 DOI: 10.1371/journal.pone.0141556] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 10/09/2015] [Indexed: 11/18/2022] Open
Abstract
Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors.
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Affiliation(s)
| | - Gloria Bueno
- VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, Spain
- * E-mail: (MMFC); (GB)
| | - Oscar Déniz
- VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Jesús Salido
- VISILAB, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Marcial García-Rojo
- Department of Pathology, Hospital de Jerez de la Frontera, Jerez de la Frontera, Cádiz, Spain
| | - Lucía González-López
- Department of Pathology, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
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Rejniak KA, Lloyd MC, Reed DR, Bui MM. Diagnostic assessment of osteosarcoma chemoresistance based on Virtual Clinical Trials. Med Hypotheses 2015; 85:348-54. [PMID: 26130106 PMCID: PMC4549200 DOI: 10.1016/j.mehy.2015.06.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 05/28/2015] [Accepted: 06/17/2015] [Indexed: 01/03/2023]
Abstract
Osteosarcoma is the most common primary bone tumor in pediatric and young adult patients. Successful treatment of osteosarcomas requires a combination of surgical resection and systemic chemotherapy, both neoadjuvant (prior to surgery) and adjuvant (after surgery). The degree of necrosis following neoadjuvant chemotherapy correlates with the subsequent probability of disease-free survival. Tumors with less than 10% of viable cells after treatment represent patients with a more favorable prognosis. However, being able to predict early, such as at the time of the pre-treatment tumor biopsy, how the patient will respond to the standard chemotherapy would provide an opportunity for more personalized patient care. Patients with unfavorable predictions could be studied in a protocol, rather than a standard setting, towards improving therapeutic success. The onset of necrotic cells in osteosarcomas treated with chemotherapeutic agents is a measure of tumor sensitivity to the drugs. We hypothesize that the remaining viable cells, i.e., cells that have not responded to the treatment, are chemoresistant, and that the pathological characteristics of these chemoresistant tumor cells within the osteosarcoma pre-treatment biopsy can predict tumor response to the standard-of-care chemotherapeutic treatment. This hypothesis can be tested by comparing patient histopathology samples before, as well as after treatment to identify both morphological and immunochemical cellular features that are characteristic of chemoresistant cells, i.e., cells that survived treatment. Consequently, using computational simulations of dynamic changes in tumor pathology under the simulated standard of care chemotherapeutic treatment, one can couple the pre- and post-treatment morphological and spatial patterns of chemoresistant cells, and correlate them with patient clinical diagnoses. This procedure, that we named 'Virtual Clinical Trials', can serve as a potential predictive biomarker providing a novel value-added decision support tool for oncologists.
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Affiliation(s)
- K A Rejniak
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Center of Excellence in Cancer Imaging and Technology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Oncologic Sciences Department, School of Medicine, University of South Florida, Tampa, FL, United States.
| | - M C Lloyd
- Analytic Microscopy Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Department of Biological Sciences, University of Illinois in Chicago, Chicago, IL, United States
| | - D R Reed
- Sarcoma Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Adolescent and Young Adult Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Oncologic Sciences Department, School of Medicine, University of South Florida, Tampa, FL, United States
| | - M M Bui
- Sarcoma Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Department of Anatomic Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Analytic Microscopy Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Oncologic Sciences Department, School of Medicine, University of South Florida, Tampa, FL, United States
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31
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Gertych A, Ing N, Ma Z, Fuchs TJ, Salman S, Mohanty S, Bhele S, Velásquez-Vacca A, Amin MB, Knudsen BS. Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput Med Imaging Graph 2015; 46 Pt 2:197-208. [PMID: 26362074 DOI: 10.1016/j.compmedimag.2015.08.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 06/30/2015] [Accepted: 08/07/2015] [Indexed: 11/18/2022]
Abstract
Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n=19) and test (n=191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN+PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J=59.5 ± 14.6 and Rand Ri=62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN=35.2 ± 24.9, OBN=49.6 ± 32, JPCa=49.5 ± 18.5, OPCa=72.7 ± 14.8 and Ri=60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.
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Affiliation(s)
- Arkadiusz Gertych
- Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA; Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA.
| | - Nathan Ing
- Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Zhaoxuan Ma
- Department of Biomedical Sciences Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Thomas J Fuchs
- Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Sadri Salman
- Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Sambit Mohanty
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Sanica Bhele
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Adriana Velásquez-Vacca
- Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Beatrice S Knudsen
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA; Department of Biomedical Sciences Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
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Sridhar A, Doyle S, Madabhushi A. Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces. J Pathol Inform 2015; 6:41. [PMID: 26167385 PMCID: PMC4498317 DOI: 10.4103/2153-3539.159441] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Accepted: 11/04/2014] [Indexed: 01/07/2023] Open
Abstract
Context: Content-based image retrieval (CBIR) systems allow for retrieval of images from within a database that are similar in visual content to a query image. This is useful for digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By representing images via a set of quantitative image descriptors, the similarity between a query image with respect to archived, annotated images in a database can be computed and the most similar images retrieved. Recently, non-linear dimensionality reduction methods have become popular for embedding high-dimensional data into a reduced-dimensional space while preserving local object adjacencies, thereby allowing for object similarity to be determined more accurately in the reduced-dimensional space. However, most dimensionality reduction methods implicitly assume, in computing the reduced-dimensional representation, that all features are equally important. Aims: In this paper we present boosted spectral embedding(BoSE), which utilizes a boosted distance metric to selectively weight individual features (based on training data) to subsequently map the data into a reduced-dimensional space. Settings and Design: BoSE is evaluated against spectral embedding (SE) (which employs equal feature weighting) in the context of CBIR of digitized prostate and breast cancer histopathology images. Materials and Methods: The following datasets, which were comprised of a total of 154 hematoxylin and eosin stained histopathology images, were used: (1) Prostate cancer histopathology (benign vs. malignant), (2) estrogen receptor (ER) + breast cancer histopathology (low vs. high grade), and (3) HER2+ breast cancer histopathology (low vs. high levels of lymphocytic infiltration). Statistical Analysis Used: We plotted and calculated the area under precision-recall curves (AUPRC) and calculated classification accuracy using the Random Forest classifier. Results: BoSE outperformed SE both in terms of CBIR-based (area under the precision-recall curve) and classifier-based (classification accuracy) on average across all of the dimensions tested for all three datasets: (1) Prostate cancer histopathology (AUPRC: BoSE = 0.79, SE = 0.63; Accuracy: BoSE = 0.93, SE = 0.80), (2) ER + breast cancer histopathology (AUPRC: BoSE = 0.79, SE = 0.68; Accuracy: BoSE = 0.96, SE = 0.96), and (3) HER2+ breast cancer histopathology (AUPRC: BoSE = 0.54, SE = 0.44; Accuracy: BoSE = 0.93, SE = 0.91). Conclusion: Our results suggest that BoSE could serve as an important tool for CBIR and classification of high-dimensional biomedical data.
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Affiliation(s)
- Akshay Sridhar
- Department of Biomedical Engineering, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Scott Doyle
- Department of Biomedical Engineering, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, USA
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Lee G, Singanamalli A, Wang H, Feldman MD, Master SR, Shih NNC, Spangler E, Rebbeck T, Tomaszewski JE, Madabhushi A. Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:284-297. [PMID: 25203987 DOI: 10.1109/tmi.2014.2355175] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the "curse of dimensionality" problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman's rank correlation which, unlike Pearson's correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6-8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.
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Nguyen K, Sarkar A, Jain AK. Prostate cancer grading: use of graph cut and spatial arrangement of nuclei. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2254-2270. [PMID: 25029379 DOI: 10.1109/tmi.2014.2336883] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Tissue image grading is one of the most important steps in prostate cancer diagnosis, where the pathologist relies on the gland structure to assign a Gleason grade to the tissue image. In this grading scheme, the discrimination between grade 3 and grade 4 is the most difficult, and receives the most attention from researchers. In this study, we propose a novel method (called nuclei-based method) that 1) utilizes graph theory techniques to segment glands and 2) computes a gland-score (based on the spatial arrangement of nuclei) to estimate how similar a segmented region is to a gland. Next, we create a fusion method by combining this nuclei-based method with the lumen-based method presented in our previous work to improve the performance of grade 3 versus grade 4 classification problem (the accuracy is now improved to 87.3% compared to 81.1% of the lumen-based method alone). To segment glands, we build a graph of nuclei and lumina in the image, and use the normalized cut method to partition the graph into different components, each corresponding to a gland. Unlike most state-of-the-art lumen-based gland segmentation method, the nuclei-based method is able to segment glands without lumen or glands with multiple lumina. Moreover, another important contribution in this research is the development of a set of measures to exploit the difference in nuclei spatial arrangement between grade 3 images (where nuclei form closed chain structure on the gland boundary) and grade 4 image (where nuclei distribute more randomly in the gland). These measures are combined to generate a single gland-score value, which estimates how similar a segmented region (which is a set of nuclei and lumina) is to a gland.
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Mosquera-Lopez C, Agaian S, Velez-Hoyos A, Thompson I. Computer-Aided Prostate Cancer Diagnosis From Digitized Histopathology: A Review on Texture-Based Systems. IEEE Rev Biomed Eng 2014; 8:98-113. [PMID: 25055385 DOI: 10.1109/rbme.2014.2340401] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Prostate cancer (PCa) is currently diagnosed by microscopic evaluation of biopsy samples. Since tissue assessment heavily relies on the pathologists level of expertise and interpretation criteria, it is still a subjective process with high intra- and interobserver variabilities. Computer-aided diagnosis (CAD) may have a major impact on detection and grading of PCa by reducing the pathologists reading time, and increasing the accuracy and reproducibility of diagnosis outcomes. However, the complexity of the prostatic tissue and the large volumes of data generated by biopsy procedures make the development of CAD systems for PCa a challenging task. The problem of automated diagnosis of prostatic carcinoma from histopathology has received a lot of attention. As a result, a number of CAD systems, have been proposed for quantitative image analysis and classification. This review aims at providing a detailed description of selected literature in the field of CAD of PCa, emphasizing the role of texture analysis methods in tissue description. It includes a review of image analysis tools for image preprocessing, feature extraction, classification, and validation techniques used in PCa detection and grading, as well as future directions in pursuit of better texture-based CAD systems.
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Lee G, Sparks R, Ali S, Shih NNC, Feldman MD, Spangler E, Rebbeck T, Tomaszewski JE, Madabhushi A. Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients. PLoS One 2014; 9:e97954. [PMID: 24875018 PMCID: PMC4038543 DOI: 10.1371/journal.pone.0097954] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 04/27/2014] [Indexed: 11/19/2022] Open
Abstract
Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly () better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms.
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Affiliation(s)
- George Lee
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States of America
- * E-mail: (GL); (AM)
| | - Rachel Sparks
- Rutgers, the State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey, United States of America
| | - Sahirzeeshan Ali
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States of America
| | - Natalie N. C. Shih
- University of Pennsylvania, Department of Pathology and Laboratory Medicine, Philadelphia, Pennslyvania, United States of America
| | - Michael D. Feldman
- University of Pennsylvania, Department of Pathology and Laboratory Medicine, Philadelphia, Pennslyvania, United States of America
| | - Elaine Spangler
- University of Pennsylvania, Department of Clinical Epidemiology and Biostatistics, Philadelphia, Pennslyvania, United States of America
| | - Timothy Rebbeck
- University of Pennsylvania, Department of Clinical Epidemiology and Biostatistics, Philadelphia, Pennslyvania, United States of America
| | - John E. Tomaszewski
- University at Buffalo, State University of New York, Department of Pathology and Anatomical Sciences, Buffalo, New York, United States of America
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States of America
- * E-mail: (GL); (AM)
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Rashid S, Fazli L, Boag A, Siemens R, Abolmaesumi P, Salcudean SE. Separation of benign and malignant glands in prostatic adenocarcinoma. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2014; 16:461-8. [PMID: 24505794 DOI: 10.1007/978-3-642-40760-4_58] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This paper presents an analysis of the high resolution histopathology images of the prostate with a focus on the evolution of morphological gland features in prostatic adenocarcinoma. Here we propose a novel technique of labeling individual glands as malignant or benign. In the first step, the gland and nuclei objects of the images are automatically segmented. Individual gland units are segmented out by consolidating their lumina with the surrounding layers of epithelium and nuclei. The nuclei objects are segmented by using a marker controlled watershed algorithm. Two new features, Number of Nuclei Layer (N(NL)) and Ratio of Epithelial layer area to Lumen area (R(EL)) have been extracted from the segmented units. The main advantage of this approach is that it can detect individual malignant gland units, irrespective of neighboring histology and/or the spatial extent of the cancer. The proposed algorithm has been tested on 40 histopathology scenes taken from 10 high resolution whole mount images and achieved a sensitivity of 0.83 and specificity of 0.81 in a leave-75%-out cross-validation.
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Affiliation(s)
- Sabrina Rashid
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Ladan Fazli
- The Vancouver Prostate Center, University of British Columbia, Vancouver, BC, Canada
| | | | | | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Septimiu E Salcudean
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
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Weakly supervised histopathology cancer image segmentation and classification. Med Image Anal 2014; 18:591-604. [PMID: 24637156 DOI: 10.1016/j.media.2014.01.010] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2012] [Revised: 12/30/2013] [Accepted: 01/28/2014] [Indexed: 11/23/2022]
Abstract
Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.
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A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am J Surg Pathol 2014; 38:128-37. [PMID: 24145650 DOI: 10.1097/pas.0000000000000086] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Human papillomavirus-related (p16-positive) oropharyngeal squamous cell carcinoma patients develop recurrent disease, mostly distant metastasis, in approximately 10% of cases, and the remaining patients, despite cure, can have major morbidity from treatment. Identifying patients with aggressive versus indolent tumors is critical. Hematoxylin and eosin-stained slides of a microarray cohort of p16-positive oropharyngeal squamous cell carcinoma cases were digitally scanned. A novel cluster cell graph was constructed using the nuclei as vertices to characterize and measure spatial distribution and cell clustering. A series of topological features defined on each node of the subgraph were analyzed, and a random forest decision tree classifier was developed. The classifier (QuHbIC) was validated over 25 runs of 3-fold cross-validation using case subsets for independent training and testing. Nineteen (11.9%) of the 160 patients on the array developed recurrence. QuHbIC correctly predicted outcomes in 140 patients (87.5% accuracy). There were 23 positive patients, of whom 11 developed recurrence (47.8% positive predictive value), and 137 negative patients, of whom only 8 developed recurrence (94.2% negative predictive value). The best other predictive features were stage T4 (18 patients; 83.1% accuracy) and N3 nodal disease (10 patients; 88.6% accuracy). QuHbIC-positive patients had poorer overall, disease-free, and disease-specific survival (P<0.001 for each). In multivariate analysis, QuHbIC-positive patients still showed significantly poorer disease-free and disease-specific survival, independent of all other variables. In summary, using just tiny hematoxylin and eosin punches, a computer-aided histomorphometric classifier (QuHbIC) can strongly predict recurrence risk. With prospective validation, this testing may be useful to stratify patients into different treatment groups.
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Jacobs JG, Panagiotaki E, Alexander DC. Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/978-3-319-10581-9_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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41
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Sparks R, Madabhushi A. Explicit shape descriptors: novel morphologic features for histopathology classification. Med Image Anal 2013; 17:997-1009. [PMID: 23850744 PMCID: PMC3811112 DOI: 10.1016/j.media.2013.06.002] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2011] [Revised: 05/31/2013] [Accepted: 06/03/2013] [Indexed: 11/25/2022]
Abstract
Object morphology, defined as shape and size characteristics, observed on medical imagery is often an important marker for disease presence and/or aggressiveness. In the context of prostate cancer histopathology, gland morphology is an integral component of the Gleason grading system which enables discrimination between low and high grade disease. However, clinicians are often unable to distinguish between subtle differences in object morphology, as evidenced by high inter-observer variability in Gleason grading. Boundary-based morphologic descriptors, such as the variance in the distance from points on the boundary of an object to its center, may not have the requisite discriminability to separate objects with subtle shape differences. In this paper, we present a set of novel explicit shape descriptors (ESDs) which are capable of distinguishing subtle shape differences between prostate glands of intermediate Gleason grades (grades 3 and 4) on prostate cancer histopathology. Calculation of ESDs involves: (1) representing object morphology using an explicit shape model (e.g. medial axis); (2) aligning the shape models via a non-rigid registration scheme with a diffeomorphic constraint and quantifying shape model dissimilarity; and (3) applying a non-linear dimensionality reduction scheme (e.g. Graph Embedding) to learn a low dimensional projection encoding the shape differences between objects. ESDs are hence the principal eigenvectors in the reduced embedding space. In this work we demonstrate that ESDs in conjunction with a Support Vector Machine classifier are able to correctly distinguish between 888 prostate glands corresponding to different Gleason grades (benign, grade 3, or grade 4) of prostate cancer from 58 needle biopsy specimens with a maximum accuracy of 0.89 and corresponding area under the receiver operating characteristic curve of 0.78.
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Affiliation(s)
- Rachel Sparks
- Rutgers University, Department of Biomedical Engineering, 599 Taylor Road, Piscataway, NJ, USA
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, 10900 Euclid Ave, Cleveland, OH, USA
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Cui W, Wang Y, Lei T, Fan Y, Feng Y. Level set segmentation of medical images based on local region statistics and maximum a posteriori probability. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:570635. [PMID: 24302974 PMCID: PMC3835522 DOI: 10.1155/2013/570635] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Revised: 09/16/2013] [Accepted: 09/23/2013] [Indexed: 11/17/2022]
Abstract
This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.
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Affiliation(s)
- Wenchao Cui
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
- College of Science, China Three Gorges University, Yichang 443002, China
| | - Yi Wang
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tao Lei
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Yangyu Fan
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Feng
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
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Kothari S, Phan JH, Stokes TH, Wang MD. Pathology imaging informatics for quantitative analysis of whole-slide images. J Am Med Inform Assoc 2013; 20:1099-108. [PMID: 23959844 PMCID: PMC3822114 DOI: 10.1136/amiajnl-2012-001540] [Citation(s) in RCA: 182] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Objectives With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. Target audience This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. Scope First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.
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Affiliation(s)
- Sonal Kothari
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Med Image Anal 2012; 17:219-35. [PMID: 23294985 DOI: 10.1016/j.media.2012.10.004] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Revised: 10/14/2012] [Accepted: 10/22/2012] [Indexed: 11/22/2022]
Abstract
Even though 1 in 6 men in the US, in their lifetime are expected to be diagnosed with prostate cancer (CaP), only 1 in 37 is expected to die on account of it. Consequently, among many men diagnosed with CaP, there has been a recent trend to resort to active surveillance (wait and watch) if diagnosed with a lower Gleason score on biopsy, as opposed to seeking immediate treatment. Some researchers have recently identified imaging markers for low and high grade CaP on multi-parametric (MP) magnetic resonance (MR) imaging (such as T2 weighted MR imaging (T2w MRI) and MR spectroscopy (MRS)). In this paper, we present a novel computerized decision support system (DSS), called Semi Supervised Multi Kernel Graph Embedding (SeSMiK-GE), that quantitatively combines structural, and metabolic imaging data for distinguishing (a) benign versus cancerous, and (b) high- versus low-Gleason grade CaP regions from in vivo MP-MRI. A total of 29 1.5Tesla endorectal pre-operative in vivo MP MRI (T2w MRI, MRS) studies from patients undergoing radical prostatectomy were considered in this study. Ground truth for evaluation of the SeSMiK-GE classifier was obtained via annotation of disease extent on the pre-operative imaging by visually correlating the MRI to the ex vivo whole mount histologic specimens. The SeSMiK-GE framework comprises of three main modules: (1) multi-kernel learning, (2) semi-supervised learning, and (3) dimensionality reduction, which are leveraged for the construction of an integrated low dimensional representation of the different imaging and non-imaging MRI protocols. Hierarchical classifiers for diagnosis and Gleason grading of CaP are then constructed within this unified low dimensional representation. Step 1 of the hierarchical classifier employs a random forest classifier in conjunction with the SeSMiK-GE based data representation and a probabilistic pairwise Markov Random Field algorithm (which allows for imposition of local spatial constraints) to yield a voxel based classification of CaP presence. The CaP region of interest identified in Step 1 is then subsequently classified as either high or low Gleason grade CaP in Step 2. Comparing SeSMiK-GE with unimodal T2w MRI, MRS classifiers and a commonly used feature concatenation (COD) strategy, yielded areas (AUC) under the receiver operative curve (ROC) of (a) 0.89±0.09 (SeSMiK), 0.54±0.18 (T2w MRI), 0.61±0.20 (MRS), and 0.64±0.23 (COD) for distinguishing benign from CaP regions, and (b) 0.84±0.07 (SeSMiK),0.54±0.13 (MRI), 0.59±0.19 (MRS), and 0.62±0.18 (COD) for distinguishing high and low grade CaP using a leave one out cross-validation strategy, all evaluations being performed on a per voxel basis. Our results suggest that following further rigorous validation, SeSMiK-GE could be developed into a powerful diagnostic and prognostic tool for detection and grading of CaP in vivo and in helping to determine the appropriate treatment option. Identifying low grade disease in vivo might allow CaP patients to opt for active surveillance rather than immediately opt for aggressive therapy such as radical prostatectomy.
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45
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Monaco JP, Madabhushi A. Class-specific weighting for Markov random field estimation: application to medical image segmentation. Med Image Anal 2012; 16:1477-89. [PMID: 22986078 PMCID: PMC3508385 DOI: 10.1016/j.media.2012.06.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2011] [Revised: 06/11/2012] [Accepted: 06/18/2012] [Indexed: 10/28/2022]
Abstract
Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/specificity). In situations where the optimal classification decision can be identified by an exhaustive search over all possible classes, means for adjusting classifier performance, such as probability thresholding or weighting the a posteriori probabilities, are well established. Unfortunately, analogous methods compatible with Markov random fields (i.e. large collections of dependent random variables) are noticeably absent from the literature. Consequently, most Markov random field (MRF) based classification systems typically restrict their performance to a single, static operating point (i.e. a paired sensitivity/specificity). To address this deficiency, we previously introduced an extension of maximum posterior marginals (MPM) estimation that allows certain classes to be weighted more heavily than others, thus providing a means for varying classifier performance. However, this extension is not appropriate for the more popular maximum a posteriori (MAP) estimation. Thus, a strategy for varying the performance of MAP estimators is still needed. Such a strategy is essential for several reasons: (1) the MAP cost function may be more appropriate in certain classification tasks than the MPM cost function, (2) the literature provides a surfeit of MAP estimation implementations, several of which are considerably faster than the typical Markov Chain Monte Carlo methods used for MPM, and (3) MAP estimation is used far more often than MPM. Consequently, in this paper we introduce multiplicative weighted MAP (MWMAP) estimation-achieved via the incorporation of multiplicative weights into the MAP cost function-which allows certain classes to be preferred over others. This creates a natural bias for specific classes, and consequently a means for adjusting classifier performance. Similarly, we show how this multiplicative weighting strategy can be applied to the MPM cost function (in place of the strategy we presented previously), yielding multiplicative weighted MPM (MWMPM) estimation. Furthermore, we describe how MWMAP and MWMPM can be implemented using adaptations of current estimation strategies such as iterated conditional modes and MPM Monte Carlo. To illustrate these implementations, we first integrate them into two separate MRF-based classification systems for detecting carcinoma of the prostate (CaP) on (1) digitized histological sections from radical prostatectomies and (2) T2-weighted 4 Tesla ex vivo prostate MRI. To highlight the extensibility of MWMAP and MWMPM to estimation tasks involving more than two classes, we also incorporate these estimation criteria into a MRF-based classifier used to segment synthetic brain MR images. In the context of these tasks, we show how our novel estimation criteria can be used to arbitrarily adjust the sensitivities of these systems, yielding receiver operator characteristic curves (and surfaces).
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Affiliation(s)
- James P. Monaco
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ, USA
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Golugula A, Lee G, Master SR, Feldman MD, Tomaszewski JE, Madabhushi A. Supervised regularized canonical correlation analysis: integrating histologic and proteomic data for predicting biochemical failures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6434-7. [PMID: 22255811 DOI: 10.1109/iembs.2011.6091588] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data, called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. In this paper, we present a novel Supervised Regularized Canonical Correlation Analysis (SRCCA) algorithm that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at risk for biochemical recurrence following radical prostatectomy. For a cohort of 19 prostate cancer patients, SRCCA was able to yield a lower fused dimensional metaspace comprising both the histological and proteomic attributes. In conjunction with SRCCA, a random forest classifier was able to identify patients at risk for biochemical failure with a maximum accuracy of 93%. The classifier performance in the SRCCA space was statistically significantly higher compared to the fused data representations obtained either with Canonical Correlation Analysis (CCA) or Regularized CCA.
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Affiliation(s)
- Abhishek Golugula
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854, USA
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47
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Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images. J Neurosci Methods 2012; 209:280-9. [DOI: 10.1016/j.jneumeth.2012.06.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2011] [Revised: 05/18/2012] [Accepted: 06/12/2012] [Indexed: 11/18/2022]
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48
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Yu E, Monaco JP, Tomaszewski J, Shih N, Feldman M, Madabhushi A. Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise Markov models. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:3427-30. [PMID: 22255076 DOI: 10.1109/iembs.2011.6090927] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper we present a system for detecting regions of carcinoma of the prostate (CaP) in H&E stained radical prostatectomy specimens using the color fractal dimension. Color textural information is known to be a valuable characteristic to distinguish CaP from benign tissue. In addition to color information, we know that cancer tends to form contiguous regions. Our system leverages the color staining information of histology as well as spatial dependencies. The color and textural information is first captured using color fractal dimension. To incorporate spatial dependencies, we combine the probability map constructed via color fractal dimension with a novel Markov prior called the Probabilistic Pairwise Markov Model (PPMM). To demonstrate the capability of this CaP detection system, we applied the algorithm to 27 radical prostatectomy specimens from 10 patients. A per pixel evaluation was conducted with ground truth provided by an expert pathologist using only the color fractal feature first, yielding an area under the receiver operator characteristic curve (AUC) curve of 0.790. In conjunction with a Markov prior, the resultant color fractal dimension + Markov random field (MRF) classifier yielded an AUC of 0.831.
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Affiliation(s)
- Elaine Yu
- Department of Biomedical Engineering, Rutgers University, USA
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49
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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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Hipp JD, Smith SC, Sica J, Lucas D, Hipp JA, Kunju LP, Balis UJ. Tryggo: Old norse for truth: The real truth about ground truth: New insights into the challenges of generating ground truth maps for WSI CAD algorithm evaluation. J Pathol Inform 2012; 3:8. [PMID: 22530176 PMCID: PMC3329067 DOI: 10.4103/2153-3539.93890] [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] [Received: 10/29/2011] [Accepted: 01/25/2012] [Indexed: 11/18/2022] Open
Affiliation(s)
- Jason D Hipp
- Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine St. Ann Arbor, Michigan 48109-0602
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