Deep learning enables prostate mri segmentation: a large cohort evaluation with inter-rater variability analysis
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort
included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ=0.75 and 0.63) and moderate agreement on apex and base segmentation (κ=0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93±0.02, significantly higher than other baseline methods, such as Deeplab v3+ and UNet (both p values <
0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.
included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ=0.75 and 0.63) and moderate agreement on apex and base segmentation (κ=0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93±0.02, significantly higher than other baseline methods, such as Deeplab v3+ and UNet (both p values <
0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.
Date Issued
2021-12-21
Date Acceptance
2021-11-11
Citation
Frontiers in Oncology, 2021, 11
ISSN
2234-943X
Publisher
Frontiers Media
Journal / Book Title
Frontiers in Oncology
Volume
11
Copyright Statement
© 2021 Liu, Miao, Surawech, Zheng, Nguyen, Yang, Raman and Sung. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Sponsor
British Heart Foundation
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Boehringer Ingelheim Ltd
Medical Research Council (MRC)
Medical Research Council (MRC)
Identifier
https://www.frontiersin.org/articles/10.3389/fonc.2021.801876/full
Grant Number
PG/16/78/32402
H2020-SC1-FA-DTS-2019-1 952172
101005122
101005122
PO:4700244755 Study:1199-0457
MR/V023799/1
MC_PC_21013
Subjects
deep attentive neural network
large cohort evaluation
prostate segmentation
qualitative evaluation
quantitative evaluation
volume measurement
1112 Oncology and Carcinogenesis
Publication Status
Published online