Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Yongkai Liu; Guang Yang; Melina Hosseiny; Afshin Azadikhah; Sohrab Afshari Mirak; Qi Miao; Steven S. Raman; Kyunghyun Sung

doi:10.1109/ACCESS.2020.3017168

IEEE Access (Jan 2020)

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Yongkai Liu,
Guang Yang,
Melina Hosseiny,
Afshin Azadikhah,
Sohrab Afshari Mirak,
Qi Miao,
Steven S. Raman,
Kyunghyun Sung

Affiliations

Yongkai Liu: ORCiD; Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
Guang Yang: ORCiD; National Heart and Lung Institute, Imperial College London, London, U.K
Melina Hosseiny: ORCiD; Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
Afshin Azadikhah: Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
Sohrab Afshari Mirak: Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
Qi Miao: Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
Steven S. Raman: Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
Kyunghyun Sung: ORCiD; Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA

DOI: https://doi.org/10.1109/ACCESS.2020.3017168
Journal volume & issue: Vol. 8
pp. 151817 – 151828

Abstract

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Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89± 0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (DeeplabV3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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