의료인공지능

Vessel visualization using MR Images without MR Angiography or Venography. Research in progress: Development of deep learning network structure to visualize brain vessel structure for surgical planning and scan time reduction. Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. We proposed deep-learned 3D BB imaging with an auto-labelling technique and 3D convolutional neural networks for brain metastases detection without additional BB scan. Intervertebral disc segmentation in MR images is challenging owing to their complex shapes and non-uniform intensity. This study introduces a robust deep-learning segmentation network which can successfully segment intervertebral discs with complex boundaries. Vertebral body segmentation on MR images provides clinically useful information including quantitative biomarkers, volume, and shape. This study proposes a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance images of the human lumbar spine. Our method achieved a 90% dice similarity coefficient and significantly reduces the user’s role. In a translational diagnostic workflow of the knee MRI involving quantitative sequences, there is a need for segmentation in order to evaluate specific regions. In the knee, articular cartilage and menisci are among those frequently segmented, to determine quantitative MR (qMR) values such as T2. Our laboratory specializes in medical imaging such as magnetic resonance imaging (MRI) and computed tomography (CT). Research on quality scoring of MR images, segmentation of area of interest, and quality enhancement of MR and CT images. We are applying artificial intelligence (Deep Learning), which is a