10,610 research outputs found
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
The segmentation of the left ventricle (LV) from CINE MRI images is essential
to infer important clinical parameters. Typically, machine learning algorithms
for automated LV segmentation use annotated contours from only two cardiac
phases, diastole, and systole. In this work, we present an analysis work-flow
for fully-automated LV segmentation that learns from images acquired through
the cardiac cycle. The workflow consists of three components: first, for each
image in the sequence, we perform an automated localization and subsequent
cropping of the bounding box containing the cardiac silhouette. Second, we
identify the LV contours using a Temporal Fully Convolutional Neural Network
(T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a
recurrent mechanism enforcing temporal coherence across consecutive frames.
Finally, we further defined the boundaries using either one of two components:
fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials
and Semantic Flow. Our initial experiments suggest that significant improvement
in performance can potentially be achieved by using a recurrent neural network
component that explicitly learns cardiac motion patterns whilst performing LV
segmentation.Comment: 6 pages, 2 figures, published on IEEE Xplor
Retinal metric: a stimulus distance measure derived from population neural responses
The ability of the organism to distinguish between various stimuli is limited
by the structure and noise in the population code of its sensory neurons. Here
we infer a distance measure on the stimulus space directly from the recorded
activity of 100 neurons in the salamander retina. In contrast to previously
used measures of stimulus similarity, this "neural metric" tells us how
distinguishable a pair of stimulus clips is to the retina, given the noise in
the neural population response. We show that the retinal distance strongly
deviates from Euclidean, or any static metric, yet has a simple structure: we
identify the stimulus features that the neural population is jointly sensitive
to, and show the SVM-like kernel function relating the stimulus and neural
response spaces. We show that the non-Euclidean nature of the retinal distance
has important consequences for neural decoding.Comment: 5 pages, 4 figures, to appear in Phys Rev Let
- …