Dual-Topological Learning for Structural Integrity Preservation in Image Segmentation

Reference No.	2026a035
Type/Category	Grant for General Research-Short-term Joint Research
Title of Research Project	Dual-Topological Learning for Structural Integrity Preservation in Image Segmentation
Principal Investigator	KANG MEIYAN（Ajou University, Korea・Ph.D Student）
Research Period
Keyword(s) of Research Fields	Topological Data Analysis (TDA), Persistent Homology, Dual-Topological Learning, Shape-aware Segmentation, Structural Integrity, Deep Learning
Objectives and Expected Results	The objective of this research is to develop a dual-objective learning framework that ensures both topological consistency and geometric shape fidelity in image segmentation. Standard pixel-wise loss functions often fail to preserve thin structures, leading to broken connectivity in complex data. By integrating Topological Loss (based on Persistent Homology) with Shape-aware constraints, this research aims to prevent structural defects at the source. The expected result is a robust segmentation pipeline capable of extracting seamless objects from intricate medical and engineering imagery, significantly improving the reliability of automated structural analysis.
Organizing Committee Members (Workshop) Participants (Short-term Joint Usage)	Shizuo Kaji（Kyoto University・Professor） Liu ran（Hebei Normal University，China・Ph.D Student）