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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Single organ segmentation filters for multiple organ segmentation.

Jacob D Furst¹, Ruchaneewan Susomboom, Daniela S Raicu

¹DePaul Univ., Chicago, IL 60604, USA. jfurst@cti.depaul.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study introduces a novel method for automatic organ segmentation in computed tomography (CT) scans. The approach effectively combines single organ segmentation with conflict resolution for accurate multi-organ identification.

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Area of Science:

Medical Imaging
Computer Vision
Computational Anatomy

Background:

Accurate organ segmentation in computed tomography (CT) data is crucial for medical diagnosis and treatment planning.
Existing methods often struggle with precise delineation of multiple organs simultaneously.

Purpose of the Study:

To develop an automated approach for multi-organ segmentation in CT images.
To enhance the accuracy and robustness of organ segmentation by resolving conflicts between individual segmentations.

Main Methods:

A three-stage process for single organ segmentation using pixel-based texture features, adaptive split-and-merge, and region growing algorithms.
A novel conflict resolution strategy comparing region sizes and average probabilities for overlapping segmented areas.
Generation of a final multiple organ segmentation from individual organ results.

Main Results:

The proposed approach enables automatic segmentation of multiple organs from CT data.
Conflict resolution effectively refines boundaries and reduces errors from single organ segmentation stages.
The method demonstrates potential for improved accuracy in complex anatomical regions.

Conclusions:

The developed method provides an effective solution for automatic multi-organ segmentation in CT.
Combining single organ segmentation with intelligent conflict resolution enhances segmentation performance.
This technique offers a valuable tool for quantitative analysis in medical imaging.