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SC-Net: Symmetrical conical network for colorectal pathology image segmentation
Gang Zhang1, Zifen He1, Yinhui Zhang1
1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Computer Methods and Programs in Biomedicine
|March 23, 2024
Summary
The Symmetric Conical Network (SC-Net) improves colorectal cancer image segmentation by effectively extracting multi-scale features and preserving spatial information, outperforming existing models. This advancement aids computer-aided medical diagnosis systems.
Area of Science:
- Medical Image Analysis
- Computer-Aided Diagnosis
- Histopathology Segmentation
Background:
- Convolutional neural networks struggle with extracting heterogeneous semantic information and context dependency across different receptive fields in histopathology images.
- Existing linear flow structures in neural networks limit the ability to capture multi-level feature variations crucial for accurate medical image diagnosis.
Purpose of the Study:
- To propose a novel Symmetric Conical Network (SC-Net) for enhanced image segmentation of colorectal cancer histopathology.
- To address limitations in extracting multi-scale, heterogeneous semantic information and establishing context dependency in existing deep learning models.
Main Methods:
- Developed a Symmetric Conical Network (SC-Net) featuring a Multi-scale Feature Extraction Block (MFEB) for diverse feature extraction.
- Employed a spiral and multi-branch arrangement of MFEBs in the encoder to improve context dependence among information flows.
- Introduced a Feature Mapping Layer (FML) to map low-level to high-level semantic features, mitigating information loss and enhancing global feature extraction.
Main Results:
- SC-Net achieved segmentation mDice scores of 0.8611 on colorectal cancer, 0.7259 on breast cancer, and 0.7144 on polyp datasets.
- Outperformed state-of-the-art models including UNet++, PSPNet, Attention U-Net, and R2U-Net in segmentation tasks.
- Demonstrated superior performance in segmenting H&E stained pathology images across various datasets.
Conclusions:
- The SC-Net effectively segments H&E stained pathology images, preserving crucial morphological and spatial information.
- The network shows robustness in handling challenging image conditions such as weak texture, poor contrast, and appearance variations.
- SC-Net represents a significant advancement in computer-aided medical image diagnosis for colorectal cancer and other pathologies.
