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Updated: Sep 14, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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AMOTS: Partially supervised framework for abdominal multi-organ and tumor segmentation via aspect-aware

Zengmin Zhang¹, Yanjun Peng¹, Xiaomeng Duan¹

¹Shandong University of Science and Technology, School of Computer Science and Engineering, Qingdao 266590, China.

Artificial Intelligence in Medicine

|July 24, 2025

Summary

Precise abdominal organ and tumor segmentation is vital for patient care. The novel AMOTS framework improves accuracy, especially with limited data, by using a cascaded approach with specialized networks and advanced training strategies.

Keywords:

Directional separation focus Hybrid supervision Multi-View Slice Cross Attention

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate abdominal organ and tumor segmentation is critical for clinical applications like surgery and radiotherapy.
Challenges include organ/tumor diversity and partially labeled datasets, hindering segmentation accuracy.
Existing methods often address missing labels but neglect network-level improvements.

Purpose of the Study:

To introduce AMOTS, a cascaded framework for precise abdominal multi-organ and pan-cancer tumor segmentation.
To enhance feature extraction and segmentation accuracy, particularly in challenging scenarios with limited labels.
To improve recognition of unlabeled classes through novel training strategies.

Main Methods:

A cascaded framework employing a lightweight convolutional network for initial localization.
Two Aspect-Aware Complementary Networks (AACNet) for fine segmentation, featuring Directional Separation Focus Module (DSFM) and Multi-View Slice Cross Attention Module (MVSCM).
Ambiguity hard mining and pseudo-label supervision strategies to address label imbalance and enhance unlabeled class recognition.

Main Results:

AMOTS demonstrated superior segmentation accuracy compared to existing methods on large public datasets (FLARE2023 and MOTS).
The proposed DSFM and MVSCM modules effectively improved boundary recognition and global interaction.
The ambiguity hard mining and pseudo-label supervision strategies enhanced performance on imbalanced datasets.

Conclusions:

The AMOTS framework offers a significant advancement in abdominal multi-organ and tumor segmentation.
The novel AACNet architecture and training strategies effectively address segmentation challenges, including data scarcity and imbalance.
AMOTS provides a robust and accurate solution for clinical applications requiring precise medical image segmentation.