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Joint Research Laboratory Yonsei-UCSD


History of JRL Yonsei-UCSD

  • 2015, Started research supervisor communication

  • 2016, graduate student participation

  • 2017, Yonsei-UCSD Joint Research Laboratory Agreement

  • 2017, Published results of research to international journals

  • 2018, Started deep research interchange and published SCI papers

  • 2019, Visited UCSD

  • 2019, Installed AI (Artificial Intelligence) program developed from Yonsei Univ. in UCSD laboratory

  • 2019, Submitted joint research results to RSNA (Radiological Society of North America)

  • 2019, Research meeting in Montreal, Canada

Disc and Vertebral Body Segmentation in Lumbar Spine MR

Fine-grain segmentation of the intervert
Fine-grain segmentation of the intervert
fig 1.png
  • 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.

Semi-Automatic Segmentation of Vertebral
  • 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.

Knee Cartilage Segmentation in Knee MR

fig 2.png
  • 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.

  • This study demonstrates an application for deep learning (DL)-based automated segmentation of cartilage and menisci. 

Co-work at UCSD

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