CT Image
Computed tomography (CT) imaging produces detailed 3D images of the body, crucial for diagnosis and treatment planning across various medical specialties. Current research emphasizes improving CT image analysis through deep learning, focusing on architectures like U-Nets, Vision Transformers, and diffusion models for tasks such as organ segmentation, lesion detection, and image enhancement. These advancements aim to increase diagnostic accuracy, improve treatment planning, reduce radiation exposure, and enable more efficient workflows in clinical settings.
Papers
November 14, 2024
October 3, 2024
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration
Thomas Buddenkotte, Roland Opfer, Julia Krüger, Alessa Hering, Mireia Crispin-Ortuzar
DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
September 18, 2024
September 16, 2024
September 4, 2024
September 1, 2024
August 30, 2024
August 7, 2024
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