Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

Toro, Óscar Alfonso Jiménez del; Atzori, Manfredo; Otálora, Sebastian; Andersson, Mats; Eurén, Kristian; Hedlund, Martin; Müller, Henning

doi:10.1117/12.2255710

Cited by 29 publications

(19 citation statements)

References 14 publications

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“…We Table 3: The network performance at the source domain. The two source networks both have better performance than [11].…”

Section: Experimental Validation and Resultsmentioning

confidence: 96%

“…The comparison between the source network and the previous study [11] is shown in Table 3. From the results, we can see both of our models have better performance than [11]. However, the study at [11] uses less WSIs than ours and the network with the best performance reported in [11] is wider and deeper than our study.…”

Section: Experimental Validation and Resultsmentioning

confidence: 99%

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Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

Ren

Hacihaliloglu

Singer

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain with-out requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.

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“…We Table 3: The network performance at the source domain. The two source networks both have better performance than [11].…”

Section: Experimental Validation and Resultsmentioning

confidence: 96%

Section: Experimental Validation and Resultsmentioning

confidence: 99%

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

Ren

Hacihaliloglu

Singer

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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“…Jiménez del Toro et al (2017) describe a completely automatic segmentation and classification method for H&E-stained tissue images. The ground truth is extracted from pathologist's reports in the original diagnoses.…”

Section: Related Workmentioning

confidence: 99%

Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology

Avenel¹,

Tamburini

Dragomir

et al. 2019

Front. Bioeng. Biotechnol.

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Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists' workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.

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“…This approach was initially evaluated for classifying Gleason grades in prostate cancer, relying only on weakly annotated data, i.e., only using the global cancer grading of WSIs without any additional manual annotations in images. [16]…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature

Schaer

Otálora

Jiménez–del–Toro

et al. 2019

Journal of Pathology Informatics

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Background: The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists. Objectives: The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text. Methods: An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features. Results: The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data sources (proprietary and public datasets, e.g., scientific literature). The system was tested by pathologists, highlighting its capabilities and suggesting ways to improve it and make it more usable in clinical practice. Conclusions: The developed system can enhance the practice of pathologists by enabling them to use their experience and knowledge to control artificial intelligence tools for navigating repositories of images for clinical decision support and teaching, where the comparison with visually similar cases can help to avoid misinterpretations. The system is available as open source, allowing the scientific community to test, ideate and develop similar systems for research and clinical practice.

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Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

Cited by 29 publications

References 14 publications

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology

Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature

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