A multi-branched semantic segmentation network based on twisted information sharing pattern for medical images

Semantic segmentation plays an indispensable role in clinical diagnosis support, intelligent surgical assistance, personalized treatment planning, and drug development, making it a core area of research in smart healthcare. However, the main challenge in medical image semantic segmentation lies in the accuracy bottleneck, primarily due to the low interactivity of feature information and the lack of deep exploration of local features during feature fusion.

To address this issue, a novel approach called Twisted Information-sharing Pattern for Multi-branched Network (TP-MNet) has been proposed. This architecture facilitates the mutual transfer of features among neighboring branches at the next level, breaking the barrier of semantic isolation and achieving the goal of semantic fusion. Additionally, performing a secondary feature mining during the transfer process effectively enhances the detection accuracy. Building upon the Twisted Pattern transmission in the encoding and decoding stages, enhanced and refined modules for feature fusion have been developed. These modules aim to capture key features of lesions by acquiring contextual semantic information in a broader context.

The experiments extensively and objectively validated the TP-MNet on 5 medical datasets and compared it with 21 other semantic segmentation models using 7 metrics. Through metric analysis, image comparisons, process examination, and ablation tests, the superiority of TP-MNet was convincingly demonstrated. Additionally, further investigations were conducted to explore the limitations of TP-MNet, thereby clarifying the practical utility of the Twisted Information-sharing Pattern.

TP-MNet adopts the Twisted Information-sharing Pattern, leading to a substantial improvement in the semantic fusion effect and directly contributing to enhanced segmentation performance on medical images. Additionally, this semantic broadcasting mode not only underscores the importance of semantic fusion but also highlights a pivotal direction for the advancement of multi-branched architectures.