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Updated: Jul 5, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Deep Learning-Based Standard Section Recognition and Multi-Organ Segmentation in Upper Abdominal Ultrasound.

Xiuming Wang¹, Lei Zhang¹, Xia Xie¹

¹Department of Ultrasound, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.

Ultrasound in Medicine & Biology

|July 3, 2026

Summary

This summary is machine-generated.

Deep learning models show promise for recognizing upper abdominal ultrasound standard sections and segmenting organs like the liver and gallbladder. Further validation is needed for broader generalizability.

Keywords:

Abdominal ultrasound Deep learning Organ segmentation Standard section recognition

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Ultrasound Technology

Background:

Upper abdominal ultrasound is crucial for diagnosing various conditions.
Standardized image acquisition and organ identification are key challenges in ultrasound interpretation.
Automating these processes can improve efficiency and consistency.

Purpose of the Study:

To develop and evaluate deep learning models for automated standard section recognition in upper abdominal ultrasound.
To implement and assess deep learning-based multi-organ segmentation following automated section recognition.
To investigate the feasibility of a two-stage deep learning framework for upper abdominal ultrasound analysis.

Main Methods:

A retrospective study utilized 465 upper abdominal ultrasound videos from a single center.
A two-stage framework involved convolutional neural network-based section recognition followed by deep learning segmentation.
Models were trained, validated, and tested on 5535 images representing 12 standard upper abdominal sections.

Main Results:

The standard section recognition model achieved high performance (e.g., 97.00% accuracy).
Organ segmentation showed strong results for the liver, gallbladder, and right kidney.
Segmentation performance was lower for the spleen and pancreas, with an IoU of 53.76% for the pancreas.

Conclusions:

Deep learning models are feasible for standard section recognition and organ segmentation in upper abdominal ultrasound using real-world data.
The developed models address complementary tasks, potentially aiding in ultrasound interpretation.
External validation is necessary to confirm the generalizability of these deep learning models.