Linchen Qian

This paper applies U-Net-based models to segment TBAD structures from CT scans, finding 3D models outperform 2D ones, with nnU-Net 3D achieving highest scores.

This paper introduces a framework for automated thoracic aorta phenotyping from CT scans, generating anatomical landmarks, meshes, and biomechanical phenotypes.

This study compares 2D and 3D U-Net for segmenting human aorta from CT images, finding 3D U-Net performs better but has geometry reconstruction errors.

This paper evaluates 15 DNN models for instance segmentation of lumbar spine MRI using a new synthetic dataset and two metrics.