Atlas-based Semantic Segmentation of Prostate Zones

Med Image Comput Comput Assist Interv. 2022 Sep;13435:570-579. doi: 10.1007/978-3-031-16443-9_55. Epub 2022 Sep 16.ABSTRACTSegmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integrate an anatomical atlas of prostate zone shape into a deep learning semantic segmentation framework to segment the CG and PZ in T2-weighted MRI. Our approach incorporates anatomical information in the form of a probabilistic prostate zone atlas and utilizes a dynamically controlled hyperparameter to combine the atlas with the semantic segmentation result. In addition to providing significantly improved segmentation performance, this hyperparameter is capable of being dynamically adjusted during the inference stage to provide users with a mechanism to refine the segmentation. We validate our approach using an external test dataset and demonstrate Dice similarity coefficient values (mean±SD) of 0.91±0.05 for the CG and 0.77±0.16 for the PZ that significantly improves upon the baseline segmentation results without the atlas. All code is publicly available on GitHub: https://github.com/OnofreyLab/prostate_atlas_segm_miccai2022.PMID:38084296 | PMC:PMC10711803 | DOI:10.1007/978-3-031-16443-9_55
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Authors: Source Type: research