3 papers
eess.IV2026
A Proof-of-Concept Study of Multitask Learning for Cranial Synthetic CT Generation Across Heterogeneous MRI Field Strengths
Zhuoyao Xin, Yiren Zhang, Christopher Wu +6
Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiothe…
eess.IV2025
TABSurfer: a Hybrid Deep Learning Architecture for Subcortical Segmentation
Aaron Cao, Vishwanatha M. Rao, Kejia Liu +3
Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans. The most accurate method, manual segmentatio…
eess.IV2024
MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation
Aaron Cao, Zongyu Li, Jordan Jomsky +2
Widely used traditional pipelines for subcortical brain segmentation are often inefficient and slow, particularly when processing large datasets. Furthermore, deep learning models…