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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
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DiffCNN: A collaborative framework of diffusion model and CNN for semi-supervised medical image segmentation.
1School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China; Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
Summary
This study introduces DiffCNN, a novel framework for semi-supervised medical image segmentation. DiffCNN combines diffusion models and CNNs to improve segmentation accuracy, especially with noisy images.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Teacher-student architectures are common in semi-supervised medical image segmentation.
- Existing methods face challenges with teacher subnet optimization and handling noisy images due to CNN limitations.
Purpose of the Study:
- To propose DiffCNN, a collaborative framework using diffusion models and CNNs for improved semi-supervised medical image segmentation.
- To address limitations of traditional teacher-student architectures in noisy medical image segmentation.
Main Methods:
- DiffCNN employs distinct CNN and diffusion subnets for collaborative learning.
- The diffusion subnet learns mask distributions to mitigate noise.
- Adversarial learning enhances the diffusion subnet's performance by aligning with real masks.
Main Results:
- DiffCNN demonstrates superior performance compared to state-of-the-art methods on three medical image segmentation datasets.
- The collaborative framework effectively extracts complementary information and handles noisy images.
Conclusions:
- DiffCNN offers a robust and effective approach for semi-supervised medical image segmentation.
- The integration of diffusion models and CNNs presents a promising direction for medical image analysis.
