PM-DUnet: Fusing long-range dependencies and attention in a dual-U architecture for thyroid nodule segmentation.

Authors

Shaoqiang Wang, Linhao Zhang, Guiling Shi, Zhongran Liu, Yuanyuan Zhang, Tiyao Liu, Yawu Zhao, Yuchen Wang, Xiaochun Cheng

Published in

PloS one. Volume 21. Issue 7. Pages e0353684. Epub Jul 30, 2026.

Abstract

For medical image segmentation, accurately balancing local details and global long-range dependencies is critical to tackling thyroid nodule challenges (variable sizes, ambiguous boundaries, complex context). Traditional CNNs excel at local feature extraction but are constrained by local receptive fields, hindering efficient global dependency modeling. To address this, we propose a Parallel Mamba Dual-U Network (PM-DUNet). It adopts a cascaded dual U-Net encoder-decoder for two-stage "coarse-to-fine" segmentation refinement. We design a Multi-Path Parallel Mamba (MPM) module-using State Space Models (SSMs)-to efficiently model global context with linear complexity. Additionally, Squeeze-Excitation Downsampling (SED) and Spatial Attention Upsampling (SAU) modules are integrated to adaptively enhance key features in encoding/decoding. Results show PM-DUNet achieves highly competitive performance and outperforms state-of-the-art methods on most core metrics, verifying its effectiveness and robustness for complex medical image segmentation. Our code is available on https://github.com/Andrevict/MPDUNet.

PMID:
42531301
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.