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Ultrasound I: Abdominal Ultrasonography

Ultrasound I: Abdominal Ultrasonography

Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...

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Updated: Jun 24, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

View adaptive unified self-supervised technique for abdominal organ segmentation.

Suchi Jain¹, Renu Dhir¹, Geeta Sikka²

¹Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India.

Computers in Biology and Medicine

|June 1, 2024

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised view-adaptive unified model (VAU-model) for automatic abdominal organ segmentation. The VAU-model significantly improves 3D context learning, enhancing segmentation accuracy for medical imaging analysis.

Keywords:

Deep learning Organ segmentation Self-supervised View-adaptive

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Automatic abdominal organ segmentation is crucial for medical diagnosis and analysis but faces challenges due to organ variability and data requirements.
Existing 3D deep learning models struggle to capture comprehensive 3D context from medical volumetric data across multiple views.
Manual segmentation is labor-intensive and time-consuming, necessitating automated solutions.

Purpose of the Study:

To propose a semi-supervised view-adaptive unified model (VAU-model) for enhanced automatic abdominal organ segmentation.
To enable 3D deep learning models to effectively learn 3D context from medical volumetric data across axial, sagittal, and coronal views.
To overcome limitations of existing contrastive learning models in capturing multi-planar contextual information.

Main Methods:

Developed a semi-supervised contrastive learning approach integrated into a unified model architecture.
Introduced a novel optimization function to facilitate learning of 3D context across multiple views within a single model.
Validated the VAU-model on diverse datasets including BTCV, NIH, and MSD.

Main Results:

The VAU-model achieved a 3.89% improvement in pancreas segmentation Dice score on the BTCV dataset compared to previous best results (81.61%).
Demonstrated strong performance on single-organ datasets, with Dice scores of 77.76% (NIH) and 76.76% (MSD) for pancreas segmentation.
Qualitative and quantitative results confirmed the model's effectiveness in abdominal organ segmentation.

Conclusions:

The proposed VAU-model effectively captures 3D context from medical volumetric data by adapting to different views in a unified manner.
This approach offers a significant advancement in semi-supervised automatic abdominal organ segmentation, outperforming existing methods.
The VAU-model shows promise for improving diagnostic accuracy and efficiency in medical practice.