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<p>Diagnosis, prognosis, and treatment of many ocular and neurodegenerative diseases,
including achromatopsia (ACHM), require the visualization of microscopic structures
in the eye. The development of adaptive optics ophthalmic imaging systems has made
high resolution visualization of ocular microstructures possible. These systems include
the confocal and split detector adaptive optics scanning light ophthalmoscope (AOSLO),
which can visualize human cone and rod photoreceptors in vivo. However, the avalanche
of data generated by such imaging systems is often too large, costly, and time consuming
to be evaluated manually, making automation necessary. The few currently available
automated cone photoreceptor identification methods are unable to reliably identify
rods and cones in low-quality images of diseased eyes, which are common in clinical
practice.</p><p>This dissertation describes the development of automated methods for
the analysis of AOSLO images, specifically focusing on cone and rod photoreceptors
which are the most commonly studied biomarker using these systems. A traditional image
processing approach, which requires little training data and takes advantage of intuitive
image features, is presented for detecting cone photoreceptors in split detector AOSLO
images. The focus is then shifted to deep learning using convolutional neural networks
(CNNs), which have been shown in other image processing tasks to be more adaptable
and produce better results than classical image processing approaches, at the cost
of requiring more training data and acting as a “black box”. A CNN based method for
detecting cones is presented and validated against state-of-the-art cone detections
methods for confocal and split detector images. The CNN based method is then modified
to take advantage of multimodal AOSLO information in order to detect cones in images
of subjects with ACHM. Finally, a significantly faster CNN based approach is developed
for the classification and detection of cones and rods, and is validated on images
from both healthy and pathological subjects. Additionally, several image processing
and analysis works on optical coherence tomography images that were carried out during
the completion of this dissertation are presented.</p><p>The completion of this dissertation
led to fast and accurate image analysis tools for the quantification of biomarkers
in AOSLO images pertinent to an array of retinal diseases, lessening the reliance
on subjective and time-consuming manual analysis. For the first time, automatic methods
have comparable accuracy to humans for quantifying photoreceptors in diseased eyes.
This is an important step in the long-term goal to facilitate early diagnosis, accurate
prognosis, and personalized treatment of ocular and neurodegenerative diseases through
optimal visualization and quantification of microscopic structures in the eye.</p>
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