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Updated: Oct 16, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

RAP-NET: COARSE-TO-FINE MULTI-ORGAN SEGMENTATION WITH SINGLE RANDOM ANATOMICAL PRIOR.

Ho Hin Lee¹, Yucheng Tang¹, Shunxing Bao¹

¹Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 20, 2021

Summary

This summary is machine-generated.

RAP-Net achieves accurate abdominal multi-organ segmentation using a novel coarse-to-fine approach. This method enhances segmentation resolution while minimizing information loss, outperforming existing models.

Keywords:

Abdominal Multi-Organ Segmentation Computed Tomography Random Anatomical Prior Single Multi-Organ Patch Model

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Current multi-organ segmentation methods often require numerous models, increasing complexity.
Loss of spatial contextual information is a challenge in high-resolution segmentation.

Purpose of the Study:

To introduce RAP-Net, a coarse-to-fine pipeline for efficient abdominal multi-organ segmentation.
To develop a single model capable of segmenting multiple abdominal organs with high fidelity.

Main Methods:

Utilizes a low-resolution coarse network for global prior context extraction from 3D volumes.
Employs a single refined model in the fine phase for segmenting all abdominal organs.
Combines anatomical priors with extracted patches to preserve location and boundary information.

Main Results:

Evaluated on a clinical cohort of 100 patient volumes with 13 annotated organs.
Achieved an average Dice score of 84.58%, outperforming the state-of-the-art (81.69%).
Demonstrated superior performance across 13 organs via 4-fold cross-validation.

Conclusions:

RAP-Net offers an effective and efficient solution for abdominal multi-organ segmentation.
The single-model approach simplifies the segmentation process while improving accuracy.
This method preserves crucial anatomical details for high-resolution segmentation.