HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
Journal:
arXiv
Published Date:
Apr 10, 2025
Abstract
Accurate segmentation of tubular structures in medical images, such as
vessels and airway trees, is crucial for computer-aided diagnosis,
radiotherapy, and surgical planning. However, significant challenges exist in
algorithm design when faced with diverse sizes, complex topologies, and (often)
incomplete data annotation of these structures. We address these difficulties
by proposing a new tubular structure segmentation framework named HarmonySeg.
First, we design a deep-to-shallow decoder network featuring flexible
convolution blocks with varying receptive fields, which enables the model to
effectively adapt to tubular structures of different scales. Second, to
highlight potential anatomical regions and improve the recall of small tubular
structures, we incorporate vesselness maps as auxiliary information. These maps
are aligned with image features through a shallow-and-deep fusion module, which
simultaneously eliminates unreasonable candidates to maintain high precision.
Finally, we introduce a topology-preserving loss function that leverages
contextual and shape priors to balance the growth and suppression of tubular
structures, which also allows the model to handle low-quality and incomplete
annotations. Extensive quantitative experiments are conducted on four public
datasets. The results show that our model can accurately segment 2D and 3D
tubular structures and outperform existing state-of-the-art methods. External
validation on a private dataset also demonstrates good generalizability.
