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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Capítulos
- 00:04Introduction
- 00:42Eyeball, Optic Nerve, and Extraocular Muscle Masking on the Orbital CT Scans
- 01:42Pre-Processing: Window Clipping and Cropping the VOIs
- 02:16Four Cross-Validations of the Orbital Segmentation Model
- 02:57Results: Segmentation Results of the Orbital Structures
- 04:01Conclusion
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.
