Journal
/
Ingenieurwesen
/
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
JoVE Journal
Ingenieurwesen
This content is Free Access.
JoVE Journal Ingenieurwesen
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

2,305 Views

•

04:48 min

•

November 30, 2022

DOI:

10.3791/64500-v

DOI:
10.3791/64500-v

•

04:48 min
November 30, 2022

•

7 Views
, , ,
1College of Medicine,The Catholic University of Korea, 2Department of Ophthalmology,St. Vincent’s Hospital, 3Yeoncheon Public Medical Center, 4Department of Industrial and Data Engineering,Hongik University, 5Department of Ophthalmology,Uijeongbu St. Mary’s Hospital

Summary

Automatically generated

An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.

Explore More Videos

Deep LearningMedical Image SegmentationOrbital Computed TomographyObject SegmentationEyeballOptic NerveExtraocular MusclesSuperpixelsSmartPencilSmartFillAutoCorrectionSequence U-NetDice ScoreVolume Similarity

Read Article

Cite this Article

Chung, Y. W., Kang, D. G., Lee, Y. O., Cho, W. Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography. J. Vis. Exp. (189), e64500, doi:10.3791/64500 (2022).

Download .ris file

Copy

Share Video

.

Copy