RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI.

Abstract

Accurate segmentation of the prostate in T2-weighted MRI is critical for effective prostate diagnosis and treatment planning. Existing methods often struggle with the complex textures and subtle variations in the prostate. To address these challenges, we propose RaNet (Residual Attention Network), a novel framework based on ResNet50, incorporating three key modules: the DilatedContextNet (DCNet) encoder, the Multi-Scale Attention Fusion (MSAF), and the Feature Fusion Module (FFM). The encoder leverages residual connections to extract hierarchical features, capturing both fine-grained details and multi-scale patterns in the prostate. The MSAF enhances segmentation by dynamically focusing on key regions, refining feature selection and minimizing errors, while the FFM optimizes the handling of spatial hierarchies and varying object sizes, improving boundary delineation. The decoder mirrors the encoder's structure, using deconvolutional layers and skip connections to retain essential spatial details. We evaluated RaNet on a prostate MRI dataset PROMISE12 and ProstateX , achieving a DSC of 98.61 and 96.57 respectively. RaNet also demonstrated robustness to imaging artifacts and MRI protocol variability, confirming its applicability across diverse clinical scenarios. With a balance of segmentation accuracy and computational efficiency, RaNet is well suited for real-time clinical use, offering a powerful tool for precise delineation and enhanced prostate diagnostics.