Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism

Author:

Ikromjanov Kobiljon1,Bhattacharjee Subrata2ORCID,Sumon Rashadul Islam1,Hwang Yeong-Byn1,Rahman Hafizur1,Lee Myung-Jae3,Kim Hee-Cheol1,Park Eunhyang4,Cho Nam-Hoon4ORCID,Choi Heung-Kook23ORCID

Affiliation:

1. Department of Digital Anti-Aging Healthcare, u-AHRC, Inje University, Gimhae 50834, Republic of Korea

2. Department of Computer Engineering, u-AHRC, Inje University, Gimhae 50834, Republic of Korea

3. JLK Artificial Intelligence R&D Center, Seoul 06141, Republic of Korea

4. Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea

Abstract

Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist’s level of expertise. In this research, we implemented a DL model using transfer learning on a set of histopathological images to segment cancerous and noncancerous areas in whole-slide images (WSIs). In this approach, the proposed Ensemble U-net model was applied for the segmentation of stroma, cancerous, and benign areas. The WSI dataset of prostate cancer was collected from the Kaggle repository, which is publicly available online. A total of 1000 WSIs were used for region segmentation. From this, 8100 patch images were used for training, and 900 for testing. The proposed model demonstrated an average dice coefficient (DC), intersection over union (IoU), and Hausdorff distance of 0.891, 0.811, and 15.9, respectively, on the test set, with corresponding masks of patch images. The manipulation of the proposed segmentation model improves the ability of the pathologist to predict disease outcomes, thus enhancing treatment efficacy by isolating the cancerous regions in WSIs.

Funder

National Research Foundation of Korea

Korea Health Industry Development Institute

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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