CENet: Context Enhancement Network for Medical Image Segmentation

TL;DR

CENet is a novel medical image segmentation framework that enhances boundary detail preservation and robustness across diverse datasets using dual enhancement blocks and multi-scale attention modules.

Contribution

The paper introduces CENet, featuring the Dual Selective Enhancement Block and Context Feature Attention Module, improving boundary accuracy and multi-organ segmentation in medical images.

Findings

Outperforms state-of-the-art methods in multi-organ segmentation

Enhances boundary detail preservation in medical images

Demonstrates robustness across radiology and dermoscopic datasets

Abstract

Medical image segmentation, particularly in multi-domain scenarios, requires precise preservation of anatomical structures across diverse representations. While deep learning has advanced this field, existing models often struggle with accurate boundary representation, variability in organ morphology, and information loss during downsampling, limiting their accuracy and robustness. To address these challenges, we propose the Context Enhancement Network (CENet), a novel segmentation framework featuring two key innovations. First, the Dual Selective Enhancement Block (DSEB) integrated into skip connections enhances boundary details and improves the detection of smaller organs in a context-aware manner. Second, the Context Feature Attention Module (CFAM) in the decoder employs a multi-scale design to maintain spatial integrity, reduce feature redundancy, and mitigate overly enhanced representations. Extensive evaluations on both radiology and dermoscopic datasets demonstrate that CENet outperforms state-of-the-art (SOTA) methods in multi-organ segmentation and boundary detail preservation, offering a robust and accurate solution for complex medical image analysis tasks. The code is publicly available at https://github.com/xmindflow/cenet.