Boundary-Emphasized Weight Maps for Distal Airway Segmentation
This work addresses segmentation accuracy for pulmonary disease diagnosis in medical imaging, though it appears incremental as it builds on existing loss function approaches.
The paper tackled the problem of automated airway segmentation from lung CT scans, where challenges like leakage, breakage, and class imbalance persist, especially in small airways. The proposed Boundary-Emphasized Loss (BEL) outperformed baseline loss functions on ATM22 and AIIB23 datasets, achieving higher topology-related metrics and comparable overall-based measures.
Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like leakage, breakage, and class imbalance persist, particularly in capturing small airways and preserving topology. We propose the Boundary-Emphasized Loss (BEL), which enhances boundary preservation using a boundary-based weight map and an adaptive weight refinement strategy. Unlike centerline-based approaches, BEL prioritizes boundary voxels to reduce misclassification, improve topology, and enhance structural consistency, especially on distal airway branches. Evaluated on ATM22 and AIIB23, BEL outperforms baseline loss functions, achieving higher topology-related metrics and comparable overall-based measures. Qualitative results further highlight BEL's ability to capture fine anatomical details and reduce segmentation errors, particularly in small airways. These findings establish BEL as a promising solution for accurate and topology-enhancing airway segmentation in medical imaging.