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Related Experiment Video

Updated: Aug 10, 2025

High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network

High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network

Published on: January 29, 2018

Hierarchical 3D Feature Learning for Pancreas Segmentation.

Federica Proietto Salanitri¹, Giovanni Bellitto¹, Ismail Irmakci^2,3

¹PeRCeiVe Lab, University of Catania, Catania, Italy.

Machine Learning in Medical Imaging. MLMI (Workshop)

|February 13, 2023

Summary

This summary is machine-generated.

We developed a 3D deep learning network for automated pancreas segmentation in CT and MRI scans. Our novel model achieves high accuracy, outperforming existing methods for CT scans and showing promising results for MRI.

Keywords:

CT and MRI pancreas segmentation Fully convolutional neural networks Hierarchical encoder-decoder architecture

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Last Updated: Aug 10, 2025

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Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

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Published on: March 4, 2011

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate pancreas segmentation is crucial for diagnosis and treatment planning.
Existing segmentation methods often struggle with the complexity and variability of pancreatic anatomy in medical scans.

Purpose of the Study:

To introduce a novel 3D fully convolutional deep network for automated pancreas segmentation.
To evaluate the model's performance on both CT and MRI imaging data.

Main Methods:

A 3D encoder extracts multi-scale volume features.
Multiple 3D decoders predict intermediate segmentation maps.
A hierarchical decoding approach combines maps for a final segmentation mask.

Main Results:

Achieved an average Dice score of approximately 88% for CT pancreas segmentation, outperforming existing methods.
Demonstrated promising performance on challenging MRI data with an average Dice score of approximately 77%.
Control experiments confirmed the effectiveness of the 3D network and hierarchical decoding.

Conclusions:

The proposed 3D fully convolutional deep network offers a robust solution for automated pancreas segmentation.
The model's architecture effectively leverages hierarchical feature representation for improved segmentation accuracy.
This approach shows significant potential for clinical applications in both CT and MRI-based pancreas analysis.