Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation.

Area of Science:

• Computer Vision
• Medical Imaging
• Machine Learning

Background:

• Variational Level Set (LS) is a common medical segmentation technique.
• Classic LS methods struggle with multi-instance objects and are sensitive to initial parameters.
• Deep learning offers potential to overcome these limitations.

Purpose of the Study:

• To introduce Recurrent Level Set (RLS) for improved contour evolution in segmentation.
• To develop Contextual RLS (CRLS) for robust semantic segmentation in diverse environments.
• To enhance segmentation accuracy, reduce computational time, and improve deep learning integration.

Main Methods:

• Proposed Recurrent Level Set (RLS) using Gated Recurrent Units within variational LS energy minimization.
• RLS models curve deformation as a hidden state evolution process.
• Extended RLS to Contextual RLS (CRLS) using shared convolutional features in an end-to-end framework.

Main Results:

• RLS demonstrated improved computational time and segmentation accuracy compared to classic variational LS methods.
• CRLS achieved competitive performance against state-of-the-art semantic segmentation approaches.
• The proposed methods effectively address limitations of traditional level set techniques.

Conclusions:

• RLS offers a significant advancement over traditional variational Level Set methods for medical image segmentation.
• CRLS provides a powerful, end-to-end solution for semantic segmentation in complex, real-world scenarios.
• The integration of recurrent neural networks with level set evolution opens new avenues for deep learning-based image analysis.