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Astigmatism and Pseudoaccommodation in Pseudophakic Eyes
noAdvanced IOLs with circumferential zones of different power provide pseudoaccommodation. We investigated the potential for power variation with meridian, namely astigmatism, to provide pseudo-accommodation. With appropriate power and axis orientations, acceptable pseudo-accommodation can be achieved
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from dB (single-frame) to dB
(denoised). For all the ONH tissues, the mean CNR increased from (single-frame) to (denoised). The MSSIM increased from
(single frame) to (denoised) when compared with
the corresponding multi-frame B-scans.
Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
The Properties of Outer Retinal Band Three Investigated With Adaptive-Optics Optical Coherence Tomography.
PurposeOptical coherence tomography's (OCT) third outer retinal band has been attributed to the zone of interdigitation between RPE cells and cone outer segments. The purpose of this paper is to investigate the structure of this band with adaptive optics (AO)-OCT.MethodsUsing AO-OCT, images were obtained from two subjects. Axial structure was characterized by measuring band 3 thickness and separation between bands 2 and 3 in segmented cones. Lateral structure was characterized by correlation of band 3 with band 2 and comparison of their power spectra. Band thickness and separation were also measured in a clinical OCT image of one subject.ResultsBand 3 thickness ranged from 4.3 to 6.4 μm. Band 2 correlations ranged between 0.35 and 0.41 and power spectra of both bands confirmed peak frequencies that agree with histologic density measurements. In clinical images, band 3 thickness was between 14 and 19 μm. Measurements of AO-OCT of interband distance were lower than our corresponding clinical OCT measurements.ConclusionsBand 3 originates from a structure with axial extent similar to a single surface. Correlation with band 2 suggests an origin within the cone photoreceptor. These two observations indicate that band 3 corresponds predominantly to cone outer segment tips (COST). Conventional OCT may overestimate both the thickness of band 3 and outer segment length
Fast, scalable, Bayesian spike identification for multi-electrode arrays
We present an algorithm to identify individual neural spikes observed on
high-density multi-electrode arrays (MEAs). Our method can distinguish large
numbers of distinct neural units, even when spikes overlap, and accounts for
intrinsic variability of spikes from each unit. As MEAs grow larger, it is
important to find spike-identification methods that are scalable, that is, the
computational cost of spike fitting should scale well with the number of units
observed. Our algorithm accomplishes this goal, and is fast, because it
exploits the spatial locality of each unit and the basic biophysics of
extracellular signal propagation. Human intervention is minimized and
streamlined via a graphical interface. We illustrate our method on data from a
mammalian retina preparation and document its performance on simulated data
consisting of spikes added to experimentally measured background noise. The
algorithm is highly accurate
Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
With the introduction of spectral-domain optical coherence tomography (OCT),
resulting in a significant increase in acquisition speed, the fast and accurate
segmentation of 3-D OCT scans has become evermore important. This paper
presents a novel probabilistic approach, that models the appearance of retinal
layers as well as the global shape variations of layer boundaries. Given an OCT
scan, the full posterior distribution over segmentations is approximately
inferred using a variational method enabling efficient probabilistic inference
in terms of computationally tractable model components: Segmenting a full 3-D
volume takes around a minute. Accurate segmentations demonstrate the benefit of
using global shape regularization: We segmented 35 fovea-centered 3-D volumes
with an average unsigned error of 2.46 0.22 {\mu}m as well as 80 normal
and 66 glaucomatous 2-D circular scans with errors of 2.92 0.53 {\mu}m
and 4.09 0.98 {\mu}m respectively. Furthermore, we utilized the inferred
posterior distribution to rate the quality of the segmentation, point out
potentially erroneous regions and discriminate normal from pathological scans.
No pre- or postprocessing was required and we used the same set of parameters
for all data sets, underlining the robustness and out-of-the-box nature of our
approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie
Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance
Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016
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