Computed Tomography
Computed tomography (CT) is a crucial medical imaging technique aiming to produce detailed cross-sectional images of the body's internal structures. Current research heavily focuses on improving image quality through advanced algorithms like diffusion models and generative adversarial networks (GANs), often incorporating physics-based simulations to address noise and artifacts, and enhancing segmentation accuracy using deep learning architectures such as U-Nets and Transformers. These advancements are significantly impacting medical diagnosis and treatment planning, particularly in oncology and interventional radiology, by enabling more precise lesion detection, improved treatment planning, and reduced radiation exposure.
Papers
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen, Talha Qaiser, Chen Jin, Fariba Yousefi, Nikolay Burlutskiy, Rossella Arcucci
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling, Marek Wodzinski
Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2
Yosuke Yamagishi, Shouhei Hanaoka, Tomohiro Kikuchi, Takahiro Nakao, Yuta Nakamura, Yukihiro Nomura, Soichiro Miki, Takeharu Yoshikawa, Osamu Abe
From Diagnostic CT to DTI Tractography labels: Using Deep Learning for Corticospinal Tract Injury Assessment and Outcome Prediction in Intracerebral Haemorrhage
Olivia N Murray, Hamied Haroon, Paul Ryu, Hiren Patel, George Harston, Marieke Wermer, Wilmar Jolink, Daniel Hanley, Catharina Klijn, Ulrike Hammerbeck, Adrian Parry-Jones, Timothy Cootes