Adaptive window adjustment with boundary DoU loss for cascade segmentation of anatomy and lesions in prostate cancer
Wenhao Li1, Bowen Zheng2, Quanyou Shen1
1School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, Guangzhou, 510006, China; Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, Guangzhou, 510006, China.
Abstract:
Accurate segmentation of prostate anatomy and lesions using biparametric magnetic resonance imaging (bpMRI) is crucial for the diagnosis and treatment of prostate cancer with the aid of artificial intelligence. In prostate bpMRI, different tissues and pathologies are best visualized within specific and narrow ranges for each sequence, which have varying requirements for image window settings. Currently, adjustments to window settings rely on experience, lacking an efficient method for universal automated adjustment. Hence, we propose an Adaptive Window Adjustment (AWA) module capable of adjusting window settings to accommodate different image modalities, sample data, and downstream tasks. Moreover, given the pivotal role that loss functions play in optimizing model performance, we investigate the performance of different loss functions in segmenting prostate anatomy and lesions. Our study validates the superiority of the Boundary Difference over Union (DoU) Loss in these tasks and extends its applicability to 3D medical imaging. Finally, we propose a cascaded segmentation approach tailored for prostate anatomy and lesions. This approach leverages anatomical structure information to enhance lesion segmentation accuracy. Experimental results on the Prostate158, ProstateX, and PI-CAI datasets confirm the effectiveness of the proposed methods. Our code of methods is available at https://github.com/WenHao-L/AWA_BoundaryDoULoss.
