Neurovascular bundles segmentation on MRI via hierarchical object activation network

Yang Lei; Tonghe Wang; Justin Roper; Sibo Tian; Pretesh Patel; Jeffrey D. Bradley; Ashesh B. Jani; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2611825

4 April 2022 Neurovascular bundles segmentation on MRI via hierarchical object activation network

Yang Lei, Tonghe Wang, Justin Roper, Sibo Tian, Pretesh Patel, Jeffrey D. Bradley, Ashesh B. Jani, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120332O (2022) https://doi.org/10.1117/12.2611825
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Sexual dysfunction after radiotherapy for prostate cancer remains an important adverse late toxicity that has been correlated with the radiation dose to the neurovascular bundles (NVBs). Currently NVBs are not contoured as an organat-risk in standard-of-care radiotherapy since they are not clearly distinguishable on planning CT images. As a result, dose to the NVBs is not optimized during treatment planning. Recently, MR images with superior soft tissue contrast have made NVB contouring feasible. In this study, we aim to develop a deep learning-based method for automated segmentation of NVBs on MR images. Our proposed method, named hierarchical object activation network, consists of four subnetworks, i.e., feature extractor, fully convolutional one-state object detector (FCOS), hierarchical block and mask module. The feature extractor is used to select the informative features from MRI. The FCOS then locates a volume of interest (VOI) for the left and right NVBs. The hierarchical block enhances the feature contrast around NVB boundary and maintains its spatial continuity. The mask module then segments the NVBs from the refined feature map within the VOI. A three-fold cross-validation study was performed using 30 patient cases to evaluate the network performance. The left and right NVBs were segmented and compared with physician contours using several segmentation metrics. The Dice similarity coefficient (DSC) and mean surface distance (MSD) are as follows: (left) 0.72, 1.64 mm, and (right) 0.72, 1.84 mm. These results demonstrate the feasibility and efficacy of our proposed method for NVB segmentation from prostate MRI, which can be further used to spare NVBs during proton and photon radiotherapy.

Citation Download Citation

Yang Lei, Tonghe Wang, Justin Roper, Sibo Tian, Pretesh Patel, Jeffrey D. Bradley, Ashesh B. Jani, Tian Liu, and Xiaofeng Yang "Neurovascular bundles segmentation on MRI via hierarchical object activation network", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332O (4 April 2022); https://doi.org/10.1117/12.2611825

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