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Related Experiment Video

Updated: Mar 28, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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SECT-Net: hybrid dual-decoder network with SE-convolution transformer for liver tumor segmentation.

Qiquan Zeng1, Dongfen Ye2, Meiqin Chen3

  • 1College of Mechanical Engineering, Quzhou University, Quzhou, China.

Frontiers in Physiology
|March 27, 2026
PubMed
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This study introduces SECT-Net, a novel hybrid dual-decoder network for accurate liver tumor segmentation. SECT-Net effectively addresses segmentation challenges, demonstrating robust performance on diverse datasets.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate liver tumor segmentation is crucial for patient prognosis and treatment planning.
  • Automated segmentation faces challenges due to tumor heterogeneity, indistinct boundaries, and varied appearances.

Purpose of the Study:

  • To develop an advanced deep learning model for precise liver tumor segmentation.
  • To overcome limitations of existing methods in segmenting complex liver tumors.

Main Methods:

  • Proposed SECT-Net, a hybrid dual-decoder network integrating squeeze-and-excitation convolution (SE-convolution) and Transformer attention.
  • Incorporated SE-convolution Transformer modules (SECTM) and a deep feature capture module (DFCM) into an encoder-decoder architecture.
Keywords:
SE-convolutionTransformer moduledeep feature capture moduledual-decoder networkencoder-decoderliver tumor segmentation

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Main Results:

  • SECT-Net achieved high segmentation performance on private datasets (Dice: 0.8452 arterial, 0.8425 portal venous) and a public dataset (Dice: 0.8845).
  • Demonstrated robust generalization capabilities with consistent Dice, Mcc, and Jaccard scores across different datasets.

Conclusions:

  • SECT-Net shows strong reliability and robustness for liver tumor segmentation.
  • The proposed network effectively handles diverse intensity distributions and morphological characteristics of liver tumors.