CSFBNet: Cosine-Consistency Filtering and Dual-Stream Complementary Semantic Guidance for Prostate Cancer MRI Segmentation.

Abstract

Accurate prostate and prostate cancer MRI segmentation remains challenging because prostate structures and lesions may be small, low-contrast, have blurred boundaries, and be strongly affected by surrounding tissues. To address these issues, this study proposes CSFBNet, a segmentation network that combines cosine-consistency-based feature filtering with foreground-background complementary semantic guidance. Specifically, the cosine-consistency sparse selection block (CSSB) selects more reliable shallow features and reduces redundant background responses. The foreground-background adaptive convolution block (FBAC) further enhances lesion-related semantic cues while suppressing background interference during decoding. Experiments were conducted on the PROMISE12, HY Prostate, and PI-CAI datasets. CSFBNet achieved Dice scores of 0.9017, 0.6539, and 0.4784, respectively, corresponding to improvements of 1.16, 5.68, and 2.13 percentage points over the baseline model. Compared with representative segmentation methods, CSFBNet achieved improved or competitive performance in region-overlap and false-positive suppression metrics. These results suggest that CSFBNet provides a practical automatic segmentation framework for prostate MRI by improving region-overlap performance and false-positive suppression, while small-lesion sensitivity and boundary recovery in challenging PI-CAI cases remain directions for future improvement.