2,467 research outputs found
Automatic segmentation of MR brain images with a convolutional neural network
Automatic segmentation in MR brain images is important for quantitative
analysis in large-scale studies with images acquired at all ages.
This paper presents a method for the automatic segmentation of MR brain
images into a number of tissue classes using a convolutional neural network. To
ensure that the method obtains accurate segmentation details as well as spatial
consistency, the network uses multiple patch sizes and multiple convolution
kernel sizes to acquire multi-scale information about each voxel. The method is
not dependent on explicit features, but learns to recognise the information
that is important for the classification based on training data. The method
requires a single anatomical MR image only.
The segmentation method is applied to five different data sets: coronal
T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age
(PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired
at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an
average age of 70 years, and T1-weighted images of young adults acquired at an
average age of 23 years. The method obtained the following average Dice
coefficients over all segmented tissue classes for each data set, respectively:
0.87, 0.82, 0.84, 0.86 and 0.91.
The results demonstrate that the method obtains accurate segmentations in all
five sets, and hence demonstrates its robustness to differences in age and
acquisition protocol
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
Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation
Celeganser: Automated Analysis of Nematode Morphology and Age
The nematode Caenorhabditis elegans (C. elegans) serves as an important model
organism in a wide variety of biological studies. In this paper we introduce a
pipeline for automated analysis of C. elegans imagery for the purpose of
studying life-span, health-span and the underlying genetic determinants of
aging. Our system detects and segments the worm, and predicts body coordinates
at each pixel location inside the worm. These coordinates provide dense
correspondence across individual animals to allow for meaningful comparative
analysis. We show that a model pre-trained to perform body-coordinate
regression extracts rich features that can be used to predict the age of
individual worms with high accuracy. This lays the ground for future research
in quantifying the relation between organs' physiologic and biochemical state,
and individual life/health-span.Comment: Computer Vision for Microscopy Image Analysis (CVMI) 202
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