Adjacency Dependent Mixture Dirichlet Process enhanced Adaptive ROI Detection with 3D CNN on DTI

Despite remarkable advances in neuroimaging, current analytical frameworks still face challenges in achieving two essential goals: clinical interpretability and computational efficiency, particularly when handling high-dimensional brain data. Although 2D approaches remain widely used, slice-by-slice analysis often fails to capture volumetric continuity, limiting the detection of subtle abnormalities across slices. Conversely, fully 3D CNN-based models demand excessive computation and memory. To overcome these limitations, we propose a 3D Adaptive Spatial Key-Region Identification (ASKRI) method that achieves both interpretability and efficiency. In this framework, key regions are adaptively enhanced within a Restricted Adjacency-Dependent Mixture Dirichlet Process model, improving interpretability while supporting clinical diagnostics. Applied to brain imaging, the method not only identifies key regions(e.g. fornix) with high classification accuracy but also isolates clinically meaningful and diagnostically informative ROIs, thereby providing a time-efficient and reliable tool for neuroimaging analysis.

3D Convolutional Neural Networks

Adaptive Spatial Key-Region Identification (ASKRI)

Universal Kriging

Restricted Adjacency Matrix

Diffusion Tensor Imaging (DTI) 

Section on Statistical Learning and Data Science