Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

Area of Science:

• Computer Vision
• Machine Learning
• Biomedical Image Analysis

Background:

• Deep learning excels at curvilinear structure segmentation (e.g., retinal blood vessels, neurites).
• Current methods using convolutional sparse coding (CSC) for filter bank learning are time-consuming, limiting scalability and adaptability.
• Slow CSC filter learning hinders the application of advanced deep learning models.

Purpose of the Study:

• To propose a novel acceleration strategy for CSC filter learning.
• To significantly reduce the computational time required for learning filter banks for curvilinear structure segmentation.
• To maintain or improve segmentation performance despite the accelerated training.

Main Methods:

• Developed a novel warm-start initialization strategy using hand-crafted filters (SCIRD-TS) that model curvilinear structure appearance.
• Refined these initial filters using convolutional sparse coding (CSC).
• Applied the accelerated CSC filter learning to auto-context regression architectures for segmentation tasks.

Main Results:

• Achieved significant time reduction in learning convolutional filter banks, up to 82%, compared to conventional methods.
• The proposed warm-start strategy resulted in filters with lower reconstruction error.
• Segmentation performance using the accelerated filters matched or exceeded that of random and DCT-based initializations.

Conclusions:

• The novel warm-start strategy effectively accelerates CSC filter learning for curvilinear structure segmentation.
• This acceleration does not compromise, and can even enhance, segmentation accuracy.
• The method offers a practical solution for faster and more adaptable deep learning model training in relevant biomedical imaging applications.