244 research outputs found
Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty
Corneal thickness (pachymetry) maps can be used to monitor restoration of
corneal endothelial function, for example after Descemet's membrane endothelial
keratoplasty (DMEK). Automated delineation of the corneal interfaces in
anterior segment optical coherence tomography (AS-OCT) can be challenging for
corneas that are irregularly shaped due to pathology, or as a consequence of
surgery, leading to incorrect thickness measurements. In this research, deep
learning is used to automatically delineate the corneal interfaces and measure
corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three
different deep learning strategies were developed based on 960 B-scans from 50
patients. On an independent test set of 320 B-scans, corneal thickness could be
measured with an error of 13.98 to 15.50 micrometer for the central 9 mm range,
which is less than 3% of the average corneal thickness. The accurate thickness
measurements were used to construct detailed pachymetry maps. Moreover,
follow-up scans could be registered based on anatomical landmarks to obtain
differential pachymetry maps. These maps may enable a more comprehensive
understanding of the restoration of the endothelial function after DMEK, where
thickness often varies throughout different regions of the cornea, and
subsequently contribute to a standardized postoperative regime.Comment: Fixed typo in abstract: The development set consists of 960 B-scans
from 50 patients (instead of 68). The B-scans from the other 18 patients were
used for testing onl
"Is social inclusion through PE, sport and PA still a rhetoric?" evaluating the relationship between physical education, sport and social inclusion
This Special Issue is part of Educational Review’s Hall of Fame, comprising the journal’s most read and highly cited papers. As part of this I will be critiquing a milestone paper within the field(s) of Sport, PE and (I will extend to) PA by Professor Richard Bailey.
The paper has been amongst the most-cited in the journal and I have personally cited the paper numerous times in my own work thus far. Upon its original publication (nearly 13 years ago), the article (managed to provide a very useful distinction between PE and sport (and PA), which is important given the constant slippage between the terms in many articles since.
In this response article, I will try to provide a brief summary of the paper from Bailey, but at the same time examine closely the notion of social inclusion through sport and PE by summarising work that has subsequently been conducted.
I will conclude by summarising that some 13 years later spurious claims about effective inclusive practices through sport abound, and we still lack clear evidence to support the rhetoric about the ways in which sport and PE can contribute to social inclusion
Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study
Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to
blindness and cardiovascular disease. Information about early stage T2D might
be present in retinal fundus images, but to what extent these images can be
used for a screening setting is still unknown. In this study, deep neural
networks were employed to differentiate between fundus images from individuals
with and without T2D. We investigated three methods to achieve high
classification performance, measured by the area under the receiver operating
curve (ROC-AUC). A multi-target learning approach to simultaneously output
retinal biomarkers as well as T2D works best (AUC = 0.746 [0.001]).
Furthermore, the classification performance can be improved when images with
high prediction uncertainty are referred to a specialist. We also show that the
combination of images of the left and right eye per individual can further
improve the classification performance (AUC = 0.758 [0.003]), using a
simple averaging approach. The results are promising, suggesting the
feasibility of screening for T2D from retinal fundus images.Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6
pages, 1 figur
Left ventricular mass assessment by CMR; how to define the optimal index
Vascular Biology and Interventio
Registration accuracy for MR images of the prostate using a subvolume based registration protocol
<p>Abstract</p> <p>Background</p> <p>In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate.</p> <p>Methods</p> <p>Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances.</p> <p>Results</p> <p>We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series.</p> <p>Conclusions</p> <p>Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.</p
Cardiovascular dynamics in ischemic cardiomyopathy during exercise
Cardiac Dysfunction and Arrhythmia
Stress imaging in patients with diabetes; routine practice?
Ventricular Dysfunction & Heart Failur
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