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Abdominal Regions and Quadrants

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In situ Quantification of Pancreatic Beta-cell Mass in Mice

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Published on: June 7, 2010

Landmark-Based Pancreas Sub-region Segmentation in CT.

Yan Zhuang^1,2, Abhinav Suri³, Tejas Sudharshan Mathai³

¹Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Journal of Imaging Informatics in Medicine

|April 30, 2025

Summary

This summary is machine-generated.

This study introduces an automated 3D tool for segmenting pancreatic sub-regions (head, body, tail) on CT scans. This enables region-specific imaging biomarkers for improved disease severity prediction in pancreatic pathologies.

Keywords:

CT Pancreas Segmentation Sub-region

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Area of Science:

Medical Imaging
Radiology
Computational Anatomy

Background:

CT-based imaging biomarkers are vital for detecting pancreatic pathologies.
Current methods lack region-specific biomarkers, hindering accurate disease severity prediction for conditions like pancreatic adenocarcinoma.
Pancreas sub-region segmentation is crucial for developing precise diagnostic tools.

Purpose of the Study:

To develop an automated 3D tool for segmenting pancreatic sub-regions (head, body, tail) on CT volumes.
To enable the derivation of region-specific imaging biomarkers for enhanced disease detection and severity assessment.
To improve the diagnostic capabilities for pancreatic pathologies through precise anatomical segmentation.

Main Methods:

A retrospective study utilized 549 CT volumes from the TotalSegmentator dataset, with 30 from the TCIA NIH Pancreas-CT dataset for external validation.
A 3D full-resolution nnUNet model was trained with a custom loss function to detect pancreatic head, body, and tail landmarks.
A post-processing algorithm generated sub-region segmentations based on detected landmarks, evaluated using Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD).

Main Results:

The model achieved high accuracy in segmenting pancreas sub-regions, with mean DSC and NSD values demonstrating robust performance on the internal dataset.
External validation on the TCIA NIH Pancreas-CT dataset confirmed the model's generalizability, yielding comparable DSC and NSD scores for all sub-regions.
The automated segmentation successfully differentiated the head, body, and tail of the pancreas, providing accurate region-specific data.

Conclusions:

The developed 3D tool accurately segments pancreatic head, body, and tail on CT volumes.
This enables the derivation of region-specific imaging biomarkers, crucial for predicting disease severity.
The automated approach enhances the potential for improved diagnosis and management of pancreatic pathologies.