HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss

• URL: http://arxiv.org/abs/2504.07827v1
• Date: Thu, 10 Apr 2025 15:04:42 GMT
• Title: HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
• Authors: Yi Huang, Ke Zhang, Wei Liu, Yuanyuan Wang, Vishal M. Patel, Le Lu, Xu Han, Dakai Jin, Ke Yan,
• Abstract summary: We propose a new tubular structure segmentation framework named HarmonySeg.<n>First, we design a deep-to-shallow decoder network featuring flexible convolution blocks with varying receptive fields.<n>Second, to highlight potential anatomical regions and improve the recall of small tubular structures, we incorporate vesselness maps.<n>Third, we introduce a topology-preserving loss function that leverages contextual and shape priors to balance the growth and suppression of tubular structures.
• Score: 37.79956077478527
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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