ACEA-Net: Weakly Supervised Prostate 3D MRI Image Segmentation via Advanced Prompt Points

In prostate 3D MRI image segmentation methods, it is usually necessary to annotate each slice, and these annotations are generally time-consuming and specialized. In this study, we generate pseudo-labels using an annotation method with one foreground seed point and six edge relaxation points. We design a weakly supervised semantic learning segmentation framework, ACEA-Net. This segmentation framework solves the under-expansion problem due to the lack of semantic affinity of the seed point pixels in the pseudo-labeling generation process. We design a Seed Cluster Geodesic Distance Transform (SeedGeo) seed expansion strategy to provide a more complete supervised signal. In the segmentation model training phase, Adaptive Convolutional Normalization (ACN) and Enhanced Simple Parameter-Free Attention Module (SimAM) are utilized to smooth the convolutional layer's output in the U-Net baseline model to suppress noisy labels. The proposed segmentation framework achieves excellent segmentation results on the MSD prostate and PROMISE12 prostate datasets, with Dice similarity coefficients (Dice) of 87.23% and 81.00% for the two segmentation tasks, and Average Symmetry Surface Distances (ASSD) of 1.73mm and 2.02mm, respectively, which are superior to the current state-of-the-art method.