CENet: Context Enhancement Network for Medical Image Segmentation
Journal:
arXiv
Published Date:
May 23, 2025
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.
