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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation.

Jubao Han^1,2, Chao Xu^1,2, Ziheng An^1,2

¹School of Integrated Circuits, Anhui University, Hefei 230601, China.

Sensors (Basel, Switzerland)

|July 9, 2022

Summary

Accurate computer-aided polyp segmentation using the novel PRAPNet improves colonoscopy outcomes. This neural network enhances polyp detection and removal, crucial for preventing cancer development.

Keywords:

colonoscopy deep learning health informatics medical image analysis polyp segmentation semantic segmentation

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Last Updated: Sep 5, 2025

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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

Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Accurate polyp segmentation in colonoscopies is vital for early cancer detection and prevention.
Computer-aided detection systems can assist endoscopists in identifying and removing abnormal tissues.
Existing segmentation models may struggle with capturing global contextual information in intestinal polyp images.

Purpose of the Study:

To propose a novel neural network, the Parallel Residual Atrous Pyramid Network (PRAPNet), for enhanced intestinal polyp segmentation.
To leverage global contextual information through a parallel residual atrous pyramid module for improved segmentation accuracy.
To evaluate the performance of PRAPNet against established segmentation models on a standard dataset.

Main Methods:

Development of PRAPNet, a neural network incorporating a parallel residual atrous pyramid module.
Utilizing the proposed module to capture global contextual information across different image regions.
Training and evaluation of PRAPNet on the Kvasir-SEG dataset for intestinal polyp segmentation.

Main Results:

PRAPNet achieved a mean intersection over union (IoU) of 90.4% and a dice coefficient of 94.2% on the Kvasir-SEG dataset.
The proposed model demonstrated superior segmentation performance compared to seven classical segmentation networks.
The parallel residual atrous pyramid module effectively utilized global contextual information for better segmentation.

Conclusions:

PRAPNet offers a significant advancement in computer-aided polyp segmentation for colonoscopies.
The model's ability to integrate global contextual information is key to its high performance.
PRAPNet shows promise in improving the accuracy and efficiency of polyp detection, potentially reducing colorectal cancer risk.