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Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth by Dai, Zhenzhen; Jambor, Ivan; Taimen, Pekka; Pantelic, Milan; Elshaikh, Mohamed; Rogers, Craig; Ettala, Otto; Boström, Peter; Aronen, Hannu; Merisaari, Harri; Wen, Ning is a scholarly article available to read on EtoBox.

What is Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth about?

Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentation accuracy. Transfer learning was investigated by fine-tuning a model trained using MRI-based delineations with prostatectomy-based delineations. Two label selection strategies were investigated in self-training. The performance of models was evaluated by 3D detection rate, dice similarity coefficient (DSC), 95 percentile Hausdrauff (95 HD, mm) and true positive ratio (TPR). Results: With prostatectomy-based delineations, the non-local Mask R-CNN with fine-tuning and self-training significantly improved all evaluation metrics. For the model with the highest detection rate and DSC, 80.5% (33/

Author

Dai, Zhenzhen; Jambor, Ivan; Taimen, Pekka; Pantelic, Milan; Elshaikh, Mohamed; Rogers, Craig; Ettala, Otto; Boström, Peter; Aronen, Hannu; Merisaari, Harri; Wen, Ning

Published

2020

Language

EN