Persistence Image from 3D Medical Image: Superpixel and Optimized Gaussian Coefficient
This work addresses the problem of capturing comprehensive 3D topological features in medical imaging for classification tasks, representing an incremental advancement over existing 2D methods.
The paper tackles the lack of 3D topological data analysis in medical imaging by proposing a method that uses superpixels and optimized Gaussian coefficients to generate persistence images from 3D volumetric data, achieving superior performance on the MedMNist3D dataset compared to traditional methods.
Topological data analysis (TDA) uncovers crucial properties of objects in medical imaging. Methods based on persistent homology have demonstrated their advantages in capturing topological features that traditional deep learning methods cannot detect in both radiology and pathology. However, previous research primarily focused on 2D image analysis, neglecting the comprehensive 3D context. In this paper, we propose an innovative 3D TDA approach that incorporates the concept of superpixels to transform 3D medical image features into point cloud data. By Utilizing Optimized Gaussian Coefficient, the proposed 3D TDA method, for the first time, efficiently generate holistic Persistence Images for 3D volumetric data. Our 3D TDA method exhibits superior performance on the MedMNist3D dataset when compared to other traditional methods, showcasing its potential effectiveness in modeling 3D persistent homology-based topological analysis when it comes to classification tasks. The source code is publicly available at https://github.com/hrlblab/TopologicalDataAnalysis3D.