615 research outputs found
Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease
The estimation of disease progression in Alzheimer’s disease
(AD) based on a vector of quantitative biomarkers is of high interest
to clinicians, patients, and biomedical researchers alike. In this work,
quantile regression is employed to learn statistical models describing the
evolution of such biomarkers. Two separate models are constructed using
(1) subjects that progress from a cognitively normal (CN) stage to mild
cognitive impairment (MCI) and (2) subjects that progress from MCI
to AD during the observation window of a longitudinal study. These
models are then automatically combined to develop a multi-stage disease
progression model for the whole disease course. A probabilistic approach
is derived to estimate the current disease progress (DP) and the disease
progression rate (DPR) of a given individual by fitting any acquired
biomarkers to these models. A particular strength of this method is that
it is applicable even if individual biomarker measurements are missing
for the subject. Employing cognitive scores and image-based biomarkers,
the presented method is used to estimate DP and DPR for subjects from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the
potential use of these values as features for different classification tasks
is demonstrated. For example, accuracy of 64% is reached for CN vs.
MCI vs. AD classification
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Identifying and interpreting fetal standard scan planes during 2D ultrasound
mid-pregnancy examinations are highly complex tasks which require years of
training. Apart from guiding the probe to the correct location, it can be
equally difficult for a non-expert to identify relevant structures within the
image. Automatic image processing can provide tools to help experienced as well
as inexperienced operators with these tasks. In this paper, we propose a novel
method based on convolutional neural networks which can automatically detect 13
fetal standard views in freehand 2D ultrasound data as well as provide a
localisation of the fetal structures via a bounding box. An important
contribution is that the network learns to localise the target anatomy using
weak supervision based on image-level labels only. The network architecture is
designed to operate in real-time while providing optimal output for the
localisation task. We present results for real-time annotation, retrospective
frame retrieval from saved videos, and localisation on a very large and
challenging dataset consisting of images and video recordings of full clinical
anomaly screenings. We found that the proposed method achieved an average
F1-score of 0.798 in a realistic classification experiment modelling real-time
detection, and obtained a 90.09% accuracy for retrospective frame retrieval.
Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical
Imagin
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis,
providing robust and accurate results by learning from annotated atlas
datasets. However, the availability of fully annotated atlas images for
training is limited due to the time required for the labelling task.
Segmentation methods requiring only a proportion of each atlas image to be
labelled could therefore reduce the workload on expert raters tasked with
annotating atlas images. To address this issue, we first re-examine the
labelling problem common in many existing approaches and formulate its solution
in terms of a Markov Random Field energy minimisation problem on a graph
connecting atlases and the target image. This provides a unifying framework for
multi-atlas segmentation. We then show how modifications in the graph
configuration of the proposed framework enable the use of partially annotated
atlas images and investigate different partial annotation strategies. The
proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets
for hippocampal and cardiac segmentation. Experiments were performed aimed at
(1) recreating existing segmentation techniques with the proposed framework and
(2) demonstrating the potential of employing sparsely annotated atlas data for
multi-atlas segmentation
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net
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