GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
This work addresses airway segmentation for medical imaging applications, offering a significant but incremental improvement over existing methods.
The paper tackles the challenge of segmenting fine-scale pulmonary bronchioles in medical images by proposing a Group Deep Dense Supervision (GDDS) method, which improves sensitivity and outperforms state-of-the-art methods by +12.8% in BD and +8.8% in TD on the BAS benchmark dataset.
Airway segmentation, especially bronchioles segmentation, is an important but challenging task because distal bronchus are sparsely distributed and of a fine scale. Existing neural networks usually exploit sparse topology to learn the connectivity of bronchioles and inefficient shallow features to capture such high-frequency information, leading to the breakage or missed detection of individual thin branches. To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner. First, Deep Dense Supervision (DDS) is proposed by constructing local dense topology skillfully and implementing dense topological learning on a specific shallow feature layer. GDDS further empowers the shallow features with better perception ability to detect bronchioles, even the ones that are not easily discernible to the naked eye. Extensive experiments on the BAS benchmark dataset have shown that our method promotes the network to have a high sensitivity in capturing fine-scale branches and outperforms state-of-the-art methods by a large margin (+12.8 % in BD and +8.8 % in TD) while only introducing a small number of extra parameters.