1,417 research outputs found
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
The health and function of tissue rely on its vasculature network to provide
reliable blood perfusion. Volumetric imaging approaches, such as multiphoton
microscopy, are able to generate detailed 3D images of blood vessels that could
contribute to our understanding of the role of vascular structure in normal
physiology and in disease mechanisms. The segmentation of vessels, a core image
analysis problem, is a bottleneck that has prevented the systematic comparison
of 3D vascular architecture across experimental populations. We explored the
use of convolutional neural networks to segment 3D vessels within volumetric in
vivo images acquired by multiphoton microscopy. We evaluated different network
architectures and machine learning techniques in the context of this
segmentation problem. We show that our optimized convolutional neural network
architecture, which we call DeepVess, yielded a segmentation accuracy that was
better than both the current state-of-the-art and a trained human annotator,
while also being orders of magnitude faster. To explore the effects of aging
and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of
cortical blood vessels in young and old mouse models of Alzheimer's disease and
wild type littermates. We found little difference in the distribution of
capillary diameter or tortuosity between these groups, but did note a decrease
in the number of longer capillary segments () in aged animals as
compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure
Detection of Hard Exudates in Retinal Fundus Images using Deep Learning
Diabetic Retinopathy (DR) is a retinal disorder that affects the people
having diabetes mellitus for a long time (20 years). DR is one of the main
reasons for the preventable blindness all over the world. If not detected early
the patient may progress to severe stages of irreversible blindness. Lack of
Ophthalmologists poses a serious problem for the growing diabetes patients. It
is advised to develop an automated DR screening system to assist the
Ophthalmologist in decision making. Hard exudates develop when DR is present.
It is important to detect hard exudates in order to detect DR in an early
stage. Research has been done to detect hard exudates using regular image
processing techniques and Machine Learning techniques. Here, a deep learning
algorithm has been presented in this paper that detects hard exudates in fundus
images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems,
Computation, Automation and Networking http://icscan.in
Open Source Software for Automatic Detection of Cone Photoreceptors in Adaptive Optics Ophthalmoscopy Using Convolutional Neural Networks
Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online
- …