DeepSSM: A blueprint for image-to-shape deep learning models.

Area of Science:

• Medical image analysis
• Computational anatomy
• Machine learning in healthcare

Background:

• Statistical shape modeling (SSM) is crucial for analyzing anatomical variations from medical images.
• Traditional SSM requires extensive pre-processing, including segmentation and registration, demanding significant human and computational resources.
• Existing methods necessitate repeating complex pipelines for new data, limiting efficiency.

Purpose of the Study:

• To introduce DeepSSM, a deep learning framework for end-to-end image-to-shape modeling.
• To automate the extraction of low-dimensional shape descriptors and representations directly from 3D medical images.
• To overcome the limitations of manual pre-processing and computational burden in conventional SSM.

Main Methods:

• Developed DeepSSM, a deep learning framework learning the mapping from images to shape descriptors and representations.
• Implemented a model-based data augmentation strategy to address data scarcity.
• Evaluated two DeepSSM architectural variants with different loss functions on three medical datasets.

Main Results:

• DeepSSM successfully infers statistical representations of anatomy directly from 3D images.
• The framework significantly reduces computational time and eliminates the need for manual segmentation.
• DeepSSM achieved comparable or superior performance to state-of-the-art SSM methods in quantitative and application-driven evaluations.

Conclusions:

• DeepSSM offers a viable, efficient, and automated solution for statistical shape modeling applications.
• The framework provides a comprehensive blueprint for deep learning-based image-to-shape models.
• DeepSSM demonstrates potential for broader clinical applications by streamlining shape analysis.