Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a multi-task edge-recalibrated network (MTER-Net), which aims to overcome the challenges, including blurry boundaries, large inter-patient appearance variations, and low soft-tissue contrast. The proposed MTER-Net is equipped with the following novel components. (a) To exploit the saliency and stability of femoral heads, we employed a light-weight localization module to locate the target region and efficiently remove the complex background. (b) We add an edge stream to the regular segmentation stream to focus on processing the edge-related information, distinguish the organs with blurry boundaries, and then boost the overall segmentation performance. Between the regular segmentation stream and edge stream, we introduce an edge recalibration module at each resolution level to connect the intermediate layers and deliver the higher-level activations from the regular stream to the edge stream to denoise the irrelevant activations. (c) Finally, using a 3D Atrous Spatial Pyramid Pooling (ASPP) feature fusion module, we fuse the features at different scales in the regular stream and the predictions from the edge stream to form the final segmentation result. The proposed segmentation network was evaluated on 200 prostate cancer patient CT images with manually delineated contours of bladder, rectum, seminal vesicle, and prostate. The segmentation performance of the proposed method was quantitatively evaluated using three metrics including Dice similarity coefficient (DSC), average surface distance (ASD), and 95% surface distance (95SD). The proposed MTER-Net achieves average DSC of 86.35%, ASD of 1.09 mm, and 95SD of 3.53 mm on the four organs, which outperforms the state-of-the-art segmentation networks by a large margin. Specifically, the quantitative DSC evaluation results of the four organs are 96.49% (bladder), 86.39% (rectum), 76.38% (seminal vesicle), and 86.14% (prostate), respectively. In conclusion, we demonstrate that the proposed MTER-Net efficiently attains superior performance to state-of-the-art pelvic organ segmentation methods.