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

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Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network.

Nikhil Kumar Tomar¹, Abhishek Srivastava², Ulas Bagci³

¹School of Computer and Information Sciences, Indira Gandhi National Open University.

Proceedings. IEEE International Symposium on Computer-Based Medical Systems

|February 13, 2023

Summary

This summary is machine-generated.

A new deep learning model, MKDCNet, enhances colorectal polyp detection during colonoscopies. This computer-aided diagnosis system improves accuracy and robustness across diverse datasets, aiding colorectal cancer prevention.

Keywords:

Deep learning colonoscopy dilated convolution multi-scale fusion polyp segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Colorectal cancer prevention relies on detecting precancerous polyps via colonoscopy.
Endoscopist variability leads to significant colorectal polyp miss rates.
Computer-aided diagnosis (CAD) systems can assist in polyp detection and reduce variability.

Purpose of the Study:

Introduce MKDCNet, a novel deep learning architecture for robust automatic polyp segmentation.
Address challenges posed by significant changes in polyp data distribution.
Develop a system to improve the accuracy and consistency of polyp detection in colonoscopies.

Main Methods:

Developed MKDCNet, an encoder-decoder neural network utilizing a pre-trained ResNet50 encoder.
Incorporated a novel multiple kernel dilated convolution (MKDC) block to expand the field of view.
Conducted extensive experiments on four public polyp datasets and a cell nuclei dataset.

Main Results:

MKDCNet demonstrated superior performance compared to state-of-the-art methods on same and unseen datasets.
The architecture proved robust to significant variations in polyp data distribution.
Achieved efficient processing at approximately 45 frames per second on an RTX 3090 GPU.

Conclusions:

MKDCNet offers a robust and efficient solution for automatic polyp segmentation.
The model shows strong potential as a benchmark for real-time clinical colonoscopy systems.
The developed system can aid in minimizing polyp miss rates and improving colorectal cancer prevention.