Grayscale self-adjusting network with weak feature enhancement for 3D lumbar anatomy segmentation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurosurgery

Background:

• Accurate segmentation of lumbar anatomy is crucial for diagnosing and treating spinal diseases.
• Deep learning has advanced lumbar segmentation, but challenges like image noise and anatomical variability persist.

Purpose of the Study:

• To develop an advanced deep neural network framework for accurate automatic segmentation of lumbar anatomy.
• To address limitations in current methods, including weak image contrast and intensity variations.

Main Methods:

• A coarse-to-fine deep neural network framework was proposed.
• Implemented a progressive refinement process to enhance feature representation in low-confidence regions.
• Introduced a grayscale self-adjusting network (GSA-Net) for dynamic intensity optimization.

Main Results:

• The proposed method demonstrated superior performance compared to existing segmentation approaches.
• Experiments on 3D CT and MR images validated the effectiveness of the framework.
• The GSA-Net component dynamically optimized image intensity distributions.

Conclusions:

• The developed deep neural network framework significantly improves lumbar anatomy segmentation accuracy.
• The method shows strong potential for enhancing the diagnosis and treatment of lumbar diseases.
• This approach offers a robust solution for challenging medical image segmentation tasks.