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A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models
Abhinav Suri1, Pritam Mukherjee2, Perry J Pickhardt3
1Radiology and Imaging Sciences, National Institutes of Health, Clinical Center, Bethesda, Maryland, USA; David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Academic Radiology
|June 29, 2024
Summary
TotalSegmentator, Abdomen Atlas, and AASwin models demonstrated strong pancreas segmentation performance. However, performance varied with scan characteristics, indicating a need for nuanced evaluation beyond aggregate metrics.
Area of Science:
- Medical Imaging and Artificial Intelligence
- Radiology and Computational Pathology
- Biomedical Engineering and Machine Learning
Background:
- Accurate pancreas segmentation on CT scans is crucial for diagnosing pancreatic diseases and developing imaging biomarkers.
- Existing studies often rely on aggregate performance metrics, potentially masking variations in model performance across different patient and scan characteristics.
Purpose of the Study:
- To benchmark the performance of five leading pancreas segmentation models using multiple metrics.
- To evaluate how segmentation performance is affected by scan characteristics, including contrast status and peri-pancreatic attenuation.
Main Methods:
- A retrospective study identified five high-performing pancreas segmentation models (TotalSegmentator, Abdomen Atlas, nnUNetv1, AASwin, DM-UNet).
- Models were evaluated on 352 CT scans using Dice score, Hausdorff distance, and average surface distance.
- Results were stratified by contrast status and peri-pancreatic attenuation; multivariate regression identified factors associated with segmentation accuracy.
Main Results:
- TotalSegmentator, Abdomen Atlas, and AASwin were top performers with Dice scores around 77-80%.
- Performance decreased on non-contrast scans for AASwin and nnUNetv1 (P < .001).
- Increasing peri-pancreatic attenuation negatively impacted Dice scores for all models except TotalSegmentator (P < .01).
Conclusions:
- Convolutional neural network-based models trained on diverse datasets (TotalSegmentator, Abdomen Atlas, AASwin) showed the best performance.
- TotalSegmentator achieved comparable results to models trained on larger datasets, highlighting training data efficiency.
- Differential performance across patient and scanning characteristics necessitates comprehensive evaluation beyond aggregate metrics for clinical applicability.
