  Accurate isolation and quantification of intraocular dimensions in the
anterior segment (AS) of the eye using optical coherence tomography (OCT)
images is important in the diagnosis and treatment of many eye diseases,
especially angle closure glaucoma. In this study, we developed a deep
convolutional neural network (DCNN) for the localization of the scleral spur,
and the segmentation of anterior segment structures (iris, corneo-sclera shell,
anterior chamber). With limited training data, the DCNN was able to detect the
scleral spur on unseen ASOCT images as accurately as an experienced
ophthalmologist; and simultaneously isolated the anterior segment structures
with a Dice coefficient of 95.7%. We then automatically extracted eight
clinically relevant ASOCT parameters and proposed an automated quality check
process that asserts the reliability of these parameters. When combined with an
OCT machine capable of imaging multiple radial sections, the algorithms can
provide a more complete objective assessment. This is an essential step toward
providing a robust automated framework for reliable quantification of ASOCT
scans, for applications in the diagnosis and management of angle closure
glaucoma.
