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MDCC-Net: Multiscale double-channel convolution U-Net framework for colorectal tumor segmentation.

Suichang Zheng¹, Xue Lin², Weifeng Zhang³

¹College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Computers in Biology and Medicine

|December 28, 2020

Summary

This summary is machine-generated.

A new Multiscale Double-Channel Convolution U-Net (MDCC-Net) improves colorectal tumor segmentation accuracy. This deep learning framework enhances feature fusion, achieving results close to expert levels for potential clinical use.

Keywords:

Colorectal cancer Feature fusion Image segmentation Multiscale U-Net

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Multiscale feature fusion is key to improving tumor segmentation accuracy.
Existing multiscale networks face challenges with insufficient feature fusion and information loss due to dense skip connections.

Purpose of the Study:

To propose a novel Multiscale Double-Channel Convolution U-Net (MDCC-Net) framework.
To enhance colorectal tumor segmentation accuracy by addressing limitations in current multiscale networks.

Main Methods:

Designed a dual-channel separation and convolution module within the coding layer.
Incorporated residual connections for multiscale feature fusion.
Fused features at different scales within the same coding layer to extract detailed image information and tumor boundaries.

Main Results:

Achieved a Dice Similarity Coefficient (DSC) of 83.57% for colorectal tumor segmentation.
Demonstrated significant improvements over existing methods: +9.59% vs. nnU-Net, +6.42% vs. U-Net, and +1.57% vs. U-Net++.

Conclusions:

The MDCC-Net framework shows strong performance in colorectal tumor segmentation, nearing expert-level accuracy.
The proposed method exhibits potential for clinical applicability in medical image analysis.