The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challeng...
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in th...
Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient's respiratory motions and predict the respiratory signals, a generalized model for predictions of d...
Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image an...
BACKGROUND: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly ...
To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated with low latency. The motion estimate is used to adapt the radiation beam to the current anatomy, yielding a more conformal dos...