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Updated: Sep 9, 2025

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

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

Published on: November 30, 2022

Polyp segmentation in colonoscopy images using DeepLabV3+.

Al Mohimanul Islam¹, Sadia Shakiba Bhuiyan¹, Mysun Mashira¹

¹Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

Computers in Biology and Medicine

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces an enhanced DeepLabv3++ model for precise polyp segmentation in colonoscopy images. The improved model significantly reduces segmentation errors, aiding early colorectal cancer detection.

Keywords:

Attention aggregation Colonoscopy images DeepLabV3+EfficientNetV2S Multi-scale feature extraction Polyp segmentation

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Published on: May 3, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Colorectal cancer is a leading cause of cancer deaths globally.
Accurate polyp segmentation in colonoscopy is crucial for early diagnosis.
Existing deep learning models struggle with small details and multi-scale feature representation.

Purpose of the Study:

To develop an enhanced DeepLabv3++ model for improved polyp segmentation in colonoscopy images.
To increase the precision and robustness of polyp detection.
To reduce segmentation errors for better clinical decision-making.

Main Methods:

Utilized EfficientNetV2S for refined feature extraction in the encoder.
Integrated Multi-Scale Pyramid Pooling (MSPP) and Parallel Attention Aggregation Block (PAAB) modules.
Implemented a redesigned decoder for enhanced feature transformation and segmentation map generation.

Main Results:

Achieved high Dice coefficient scores: 96.20% (CVC-ColonDB), 96.54% (CVC-ClinicDB), and 96.08% (Kvasir-SEG).
Outperformed several state-of-the-art models in polyp segmentation.
Significantly reduced segmentation errors (false positives/negatives) for polyps of all sizes compared to the baseline DeepLabv3+.

Conclusions:

The enhanced DeepLabv3++ model demonstrates superior performance in colonoscopy polyp segmentation.
The integration of MSPP and a redesigned decoder improves the model's ability to capture multi-scale and directional features.
This advancement is vital for accurate polyp delineation and clinical decision-making in colorectal cancer screening.