Topology-Aware Learning for Robust Tubular Structure Segmentation In 3D Optical Microscopy
To address the persistent loss of connectivity and thickness instability in 3D microscopy segmentation of tubular structures, we propose a topology-aware learning framework that explicitly supervises tubular geometry beyond voxel-wise overlap.
Poster Program · Diagnostic and Interventional Radiology Physics
ZA-Net: Zero-Annotation Nuclei Segmentation In Pathology Images with Vision–Language Pretrained Models
Nuclei segmentation in histopathology images is fundamental for cancer diagnosis and quantitative analysis, yet existing supervised and weakly supervised methods require extensive manual annotations. Although vision–language pre-trained models enable zero-sho...
Poster Program · Diagnostic and Interventional Radiology Physics