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Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and

Elanchezhian Somasundaram^1,2, Zachary Taylor¹, Vinicius V Alves¹

¹Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229.

AJR. American Journal of Roentgenology

|May 1, 2024

Summary

Deep learning models for pediatric abdominal organ segmentation, utilizing transfer learning (TL) on public datasets and fine-tuning with institutional data, showed superior performance compared to native training and existing models. This advancement offers potential for improved pediatric imaging volumetry applications.

Keywords:

children deep learning liver segmentation spleen

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Area of Science:

Medical image analysis
Deep learning in radiology
Pediatric CT segmentation

Background:

Deep learning algorithms excel in adult abdominal organ segmentation.
Validation of these algorithms in pediatric populations is limited.
Pediatric CT examinations require specialized segmentation models.

Purpose of the Study:

To develop and validate deep learning models for segmenting liver, spleen, and pancreas in pediatric CT scans.
To compare the performance of different deep learning architectures and training strategies.

Main Methods:

Retrospective analysis of 1731 CT examinations (483 pediatric, 1248 mixed pediatric/adult).
Training and validation of SegResNet, DynUNet, and SwinUNETR models using native training (NT) and transfer learning (TL).
Comparison with TotalSegmentator; performance evaluated using Dice Similarity Coefficient (DSC).

Main Results:

Transfer learning (TL) models outperformed native training (NT) and TotalSegmentator on internal and public pediatric datasets.
Segmentation performance was highest for the liver and spleen, followed by the pancreas.
The DynUNet TL model demonstrated the best overall performance and was released as open-source.

Conclusions:

Transfer learning models, fine-tuned on institutional pediatric data, significantly improve abdominal organ segmentation in children.
The developed open-source model offers a valuable tool for pediatric imaging analysis.
Segmentation accuracy varies by organ, with the pancreas being more challenging.