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Updated: Jan 8, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
The INI-32 dataset: An annotated computed tomography atlas of the orbit
Vishal Patel1, Manisha Kotay2, Joy Li3
1University of Southern California, Department of Radiology, 1500 San Pablo St, 2nd Floor, Los Angeles, CA, 90033 USA.
None:
We introduce INI-32, a publicly available computed tomography (CT) atlas of the orbit intended to facilitate reproducible volumetric studies of orbital soft tissues. Sixteen routine clinical CTs with normal orbital anatomy were retrospectively selected, and left orbits were mirrored to yield 32 right-orbit volumes. These volumes were spatially normalized and averaged to create a template. Neuroradiologists and orbital surgeons annotated key orbital structures on the template, and a consensus segmentation was produced using simultaneous truth and performance level estimation (STAPLE). The INI-32 atlas consists of the normalized average CT volume and consensus label map in the Neuroimaging Informatics Technology Initiative (NIfTI) format, together with a label description file. The dataset supports atlas-based segmentation, reproducible morphometry, and provides a reference coordinate system for orbital research. The atlas is available from the NeuroImaging Tools and Resources Collaboratory (NITRC) at https://www.nitrc.org/projects/ini32_orbit_ct.
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