LABEL-SAM: A Semi-Automatic Interactive Annotation Model for Aortic Dissection Segmentation in 3D CTA Image

Abstract

Aortic Dissection (AD) is a life-threatening disease that can be rapidly screened by using deep learning methods. However, deep learning model training requires a large amount of manual annotation of data. To improve the annotation efficiency and accuracy, we propose LABEL-SAM, a semi-automatic interactive segmentation algorithm designed to efficiently annotate AD in 3D computed tomography angiography (CTA) images. By requiring minimal user input—only points and bounding boxes on the first and last slices—LABEL-SAM automatically generates segmentation prompts for intermediate slices, reducing the manual workload. In addition, we propose a bidirectional prediction weighting method and a fine-tuning strategy tailored to AD data, further enhancing segmentation accuracy. Furthermore, LABEL-SAM is implemented as a plug-and-play plugin for the 3D Slicer software. Experimental results on both external and internal datasets demonstrate the method’s superior performance, improving annotation accuracy and efficiency. The code will be available at https://github.com/wenjiecai/LABEL-SAM. The demonstration video is now available at https://www.youtube.com/watch?v=R3Fzgl1b4JQ.