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MCDNet: Morphological-conditional dual-view fusion for 3D tubular structure segmentation.

Zhiyan Wang1, Changjian Wang1, Kele Xu1

  • 1College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 30, 2026
PubMed
Summary

A new deep learning model, MCDNet (Morphological-Conditional Dual-view Network), enhances 3D medical image segmentation for tubular structures. It improves accuracy across diverse anatomical regions by integrating morphological and contextual information, outperforming existing methods.

Keywords:
Conditional convolutionDual-view architectureTubular structure segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate segmentation of 3D tubular structures is vital for medical diagnosis and treatment planning.
  • Current deep learning models struggle with generalizability across different anatomical regions due to reliance on specific morphological priors.
  • Joint modeling of global and local morphological characteristics in tubular structure segmentation is underexplored.

Purpose of the Study:

  • To develop a novel deep learning network, MCDNet, for generalizable 3D tubular structure segmentation.
  • To integrate both contextual and morphological information effectively for improved segmentation performance.
  • To address the limitations of existing methods in handling diverse tubular geometries and anatomical variations.

Main Methods:

  • Proposed MCDNet (Morphological-Conditional Dual-view Network) incorporating a target-adaptive Morphological-Conditional Convolution (MCConv).
  • Implemented a three-stage architecture: morphological feature extraction, contextual feature learning with cross-fusion, and residual self-attention fusion.
  • MCConv enhances structural sensitivity across diverse tubular shapes, while cross-fusion combines convolutional and attention-based representations.

Main Results:

  • MCDNet achieved superior performance on four diverse benchmark datasets for tubular segmentation.
  • Demonstrated an average Dice coefficient improvement of 6.93% compared to a strong baseline.
  • Showcased a 10.61% reduction in Hausdorff distance, indicating improved boundary accuracy.

Conclusions:

  • MCDNet offers a robust and generalizable solution for 3D tubular structure segmentation in medical imaging.
  • The integration of morphological and contextual features via MCConv and dual-view architecture is key to its improved performance.
  • The proposed method advances the state-of-the-art in medical image segmentation for complex anatomical structures.