Development and Validation of AI System for AAS CTA Diagnosis and Mapping

AAS-DSS: AI-Powered Acute Aortic Syndrome Decision Support System Code This repository contains the code for AAS-DSS, a Python-based pipeline for automated detection/classification of acute aortic syndrome (AAS) subtypes (AD, IMH, PAU) using CTA images. Core Functions: - Preprocessing & aorta mask generation via TotalSegmentator. - Multi-class segmentation (AD/IMH/PAU) with nnUNet (requires pre-trained weights). - Slice-level classification using custom .pth models. - User-friendly GUI for path configuration and pipeline execution. Prerequisites: Python 3.x, medical imaging libraries, TotalSegmentator, pre-trained model weights (per specified directories). Input/Output: Accepts .nii.gz CTA images; outputs results to a user-specified folder. Flexible path adjustment supported. AAS-DSS enables automated 17-zone aortic segmentation (extended SVS/STS classification) to support standardized AAS diagnosis.