7 papers
SynthRAR: Ring Artifacts Reduction in CT with Unrolled Network and Synthetic Data Training
Hongxu Yang, Levente Lippenszky, Edina Timko +1
Defective and inconsistent responses in CT detectors can cause ring and streak artifacts in the reconstructed images, making them unusable for clinical purposes. In recent years, s…
Low performing pixel correction in computed tomography with unrolled network and synthetic data training
Hongxu Yang, Levente Lippenszky, Edina Timko +2
Low performance pixels (LPP) in Computed Tomography (CT) detectors would lead to ring and streak artifacts in the reconstructed images, making them clinically unusable. In recent y…
Synthetic Volumetric Data Generation Enables Zero-Shot Generalization of Foundation Models in 3D Medical Image Segmentation
Satrajit Chakrabarty, Sourya Sengupta, Gopal Avinash +1
Foundation models such as Segment Anything Model 2 (SAM 2) exhibit strong generalization on natural images and videos but perform poorly on medical data due to differences in appea…
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Sourya Sengupta, Satrajit Chakrabarty, Keerthi Sravan Ravi +2
Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in textur…
SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging
Danielle L. Ferreira, Ahana Gangopadhyay, Hsi-Ming Chang +2
Accurate segmentation of anatomical structures in ultrasound (US) images, particularly small ones, is challenging due to noise and variability in imaging conditions (e.g., probe po…
Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping
Hongxu Yang, Najib Akram Aboobacker, Xiaomeng Dong +3
X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by…