A lightweight network for segmenting tree-like structures in medical images

Segmentation of medical images containing tree-like structures is critical for disease diagnosis, simulation modelling and surgical planning. Although many deep learning methods have achieved impressive performance in this domain, challenges remain due to the elongated and complex geometry of such structures, which are prone to disconnection during segmentation. Most existing methods rely on convolutional neural networks, which struggle to capture long-range dependencies inherent in tree-like structures. Transformer-based approaches offer improvements in this respect but suffer from quadratic computational complexity as input size increases. Recently, Mamba has emerged as a promising alternative, offering comparable performance to Transformers with linear computational complexity, and has started gaining attention in medical image segmentation. However, prior work has mainly focused on organ or lesion segmentation. In this work, we propose CoorMa, a lightweight U-Net-shaped model that integrates Coordinate Attention and Mamba Aggregation Modules for the segmentation of tree-like structures in medical images. Coordinate Attention enhances directional feature extraction, while the Mamba Aggregation Modules fuse features across scales to better capture long-range dependencies. Additionally, we introduce a skeleton-based artefact removal algorithm to eliminate spurious segmentation artefacts while preserving structural integrity. We evaluate our method on four 2D datasets (DCA1, CHUAC, DRIVE, CHASE_DB1) and one 3D dataset (Binary Airway Segmentation). Experimental results show that CoorMa outperforms state-of-the-art methods, achieving the highest evaluation metrics with significantly fewer parameters.