Cascaded neural network segmentation pipeline for automated delineation of prostate and organs at risk in male pelvic CT

Abstract

Delineation of the prostate and nearby organs at risk (OARs) is a fundamental step in prostate cancer radiation therapy planning. Such contouring is often done manually, which can be a time-consuming and highly variable process. To alleviate these issues, we propose a fully automated two-step deep learning approach to segment the prostate, bladder, rectum, seminal vesicles, and femoral heads from CT images. The first step localizes the organs of interest using a modified 3D UNet architecture that contains an axial cross-attention module. Final segmentations are then computed for each organ individually using organ-specifically optimized UNet-based models. A total of 275 CT images were used for model training and validation. When evaluated on a hold-out set of 15 image sets, the full pipeline achieved mean dice similarity coefficients (DSC) and 95% Hausdorff distances (95HD, in mm) of 0.8660.034 and 4.461.02 (prostate), 0.9570.014 and 2.910.289 (bladder), 0.8530.044 and 5.101.87 (rectum), 0.7400.117 and 6.729.46 (seminal vesicles), 0.9420.016 and 2.851.04 (left femoral head), 0.9420.018 and 3.041.37 (right femoral head).

Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography

“…We previously presented the utility of such a twostep approach. 52 We extend this work by showing that segmentation performance can be improved by propagating information from the first stage of the segmentation pipeline to the second. We introduce an image enhancement module that enhances input images to the fine segmentation using the output probabilities from the multi-organ segmentation step, emphasizing difficult-to-segment regions.…”

“…In this paper, we show that ACA-UNet does a good job at segmenting organs from male pelvic CT and that segmentation performance can be improved by utilizing ACA-UNet as the first step in a two-step segmentation pipeline. We previously presented the utility of such a two-step approach 52 . We extend this work by showing that segmentation performance can be improved by propagating information from the first stage of the segmentation pipeline to the second.…”