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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

PAPNet: Convolutional network for pancreatic cyst segmentation.

Jin Li¹, Wei Yin², Yuanjun Wang¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Journal of X-Ray Science and Technology

|April 11, 2023

Summary

This summary is machine-generated.

This study introduces PAPNet, a novel deep learning model for segmenting pancreatic cysts in CT scans. PAPNet achieves high accuracy, aiding in computer-aided diagnosis of pancreatic conditions.

Keywords:

Pancreatic cyst computed tomography convolutional neural network medical image segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate segmentation of the pancreas and tumors is crucial for computer-aided diagnosis.
Pancreatic cyst segmentation in CT scans is challenging due to variations in cyst location and shape.

Purpose of the Study:

To develop an effective method for segmenting pancreatic cysts in abdominal CT scans.
To improve the accuracy of computer-aided diagnosis for pancreatic conditions.

Main Methods:

Proposed a novel convolutional neural network architecture named Pyramid Attention and Pooling on Convolutional Neural Network (PAPNet).
Introduced an atrous pyramid attention module for multi-scale feature extraction.
Utilized a spatial pyramid pooling module for fusing contextual spatial information.

Main Results:

PAPNet was trained and tested on 1,346 CT slices from 107 patients.
Achieved a mean Dice Similarity Coefficient (DSC) of 84.53% and a mean Jaccard Index (JI) of 75.81% via 5-fold cross-validation.

Conclusions:

The proposed PAPNet method demonstrates effective performance for pancreatic cyst segmentation.
The findings suggest PAPNet's potential to enhance computer-aided diagnostic capabilities for pancreatic diseases.