ArticleQuantitative imaging in medicine and surgery2025
Automatic segmentation and reconstruction of lower-extremity arteries from computed tomography angiography images via a deep learning framework.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Preoperative lower extremity computed tomography angiography (CTA) is indispensable for planning free skin flap transplantation in patients with oral and maxillofacial tumors. However, there is a lack of dedicated automatic three-dimensional (3D) reconstruction tools of lower-extremity arteries from CTA images. Our study aimed to develop and validate an artificial intelligence (AI) model based on a 3D convolutional neural network (CNN) for automatic reconstruction of lower-extremity arteries from preoperative lower extremity CTA images. Methods: This retrospective study included a dataset of lower extremity CTA images from 1,201 patients with oral or maxillofacial tumors between January 2015 and December 2023. A deep learning-based lower-extremity artery segmentation network (LEAS-Net) was proposed for 3D reconstruction of the lower-extremity arteries from CTA images, which had a three-stage network architecture consisting of a coarse-resolution network for initial vessel localization, a refinement network for skeleton extraction, and a fine-resolution network for precise segmentation. The segmentation performance of LEAS-Net was assessed via the Dice similarity coefficient and center-line Dice coefficient (clDice). The quality of reconstructed images and the time needed for reconstruction were compared between LEAS-Net and three human radiologists. Results: The LEAS-Net exhibited high accuracy in segmenting large vessels and small perforator vessels at the voxel level, with average Dice and clDice coefficients exceeding 0.65. The LEAS-Net achieved a higher or comparable image quality score for the reconstruction of large vessels and a higher quality score for the perforator vessel reconstruction compared with human radiologists (P<0.01). The processing time of LEAS-Net was reduced by 9.7 to 22 times compared with the three human radiologists (P<0.05). Conclusions: The LEAS-Net can be used as an AI tool to reconstruct lower-extremity arteries from CTA images for planning perforator flap transplantation.
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