333 research outputs found
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging
marker for cerebral small vessel disease, and have been shown to be related to
increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into
its etiology and its potential as a risk indicator of disease. We propose a
convolutional network regression method to quantify the extent of EPVS in the
basal ganglia from 3D brain MRI. We first segment the basal ganglia and
subsequently apply a 3D convolutional regression network designed for small
object detection within this region of interest. The network takes an image as
input, and outputs a quantification score of EPVS. The network has
significantly more convolution operations than pooling ones and no final
activation, allowing it to span the space of real numbers. We validated our
approach using a dataset of 2000 brain MRI scans scored visually. Experiments
with varying sizes of training and test sets showed that a good performance can
be achieved with a training set of only 200 scans. With a training set of 1000
scans, the intraclass correlation coefficient (ICC) between our scoring method
and the expert's visual score was 0.74. Our method outperforms by a large
margin - more than 0.10 - four more conventional automated approaches based on
intensities, scale-invariant feature transform, and random forest. We show that
the network learns the structures of interest and investigate the influence of
hyper-parameters on the performance. We also evaluate the reproducibility of
our network using a set of 60 subjects scanned twice (scan-rescan
reproducibility). On this set our network achieves an ICC of 0.93, while the
intrarater agreement reaches 0.80. Furthermore, the automatic EPVS scoring
correlates similarly to age as visual scoring
3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automated EPVS scoring correlates similarly to age as visual scoring
Hydranet: Data Augmentation for Regression Neural Networks
Deep learning techniques are often criticized to heavily depend on a large
quantity of labeled data. This problem is even more challenging in medical
image analysis where the annotator expertise is often scarce. We propose a
novel data-augmentation method to regularize neural network regressors that
learn from a single global label per image. The principle of the method is to
create new samples by recombining existing ones. We demonstrate the performance
of our algorithm on two tasks: estimation of the number of enlarged
perivascular spaces in the basal ganglia, and estimation of white matter
hyperintensities volume. We show that the proposed method improves the
performance over more basic data augmentation. The proposed method reached an
intraclass correlation coefficient between ground truth and network predictions
of 0.73 on the first task and 0.84 on the second task, only using between 25
and 30 scans with a single global label per scan for training. With the same
number of training scans, more conventional data augmentation methods could
only reach intraclass correlation coefficients of 0.68 on the first task, and
0.79 on the second task.Comment: accepted in MICCAI 201
Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces
BACKGROUND: Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified.NEW METHOD: We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T.RESULTS: The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter.COMPARISON WITH EXISTING METHODS: Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given.CONCLUSIONS: Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.</p
Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network
In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects’ T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS—demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.ope
Characterizing and revealing biomarkers on patients with Cerebral Amyloid Angiopathy using artificial intelligence
Dissertação de mestrado em BioinformáticaCerebral Amyloid Angiopathy is a cerebrovascular disorder resulting from the deposition of an
amyloidogenic protein in small and medium sized cortical and leptomeningeal vessels. A
primary cause of spontaneous intracerebral haemorrhages, it manifests predominantly in the
elder population. Although CAA is a common neuropathological finding on itself, it is also
known to frequently occur in conjunction with Alzheimer’s disease, being sometimes
misdiagnosed.
Currently, CAA diagnosis is generally conducted by post-mortem examination or, in live
patients by the examination of an evacuated hematoma or brain biopsy samples, which are
typically unavailable. Therefore, a reliable and non-invasive method for diagnosing CAA would
facilitate the clinical decision making and accelerate the clinical intervention.
The main goal of this dissertation is to study the application of Machine Learning (ML) to reveal
possible biomarkers to aid the diagnosis and early medical intervention, and better
understand the disease. Therefore, three scenarios were tested: Classification of four
neurodegenerative diseases with annotation data obtained from visual rating scores, age and
gender; Classification of the diseases with radiomic data derived from the patient’s MRI; and
a combination of the previous experiments. The results show that the application of Artificial
intelligence in the medical field brings advantages to support the physicians in the decision making process and, at some point, make a correct prediction of the disease label.
Although the results are satisfactory, there are still improvements to be done. For instance,
image segmentation of cerebral lesions or brain regions and additional clinical information of
the patients would be of value.Angiopatia Amiloide Cerebral (AAC) é uma doença vascular cerebral resultante da deposição
de matéria amiloide. Principal causa de hemorragias cerebral espontâneas, a AAC manifesta se predominantemente na população idosa. Embora a AAC seja uma doença que por si só tem
um grande impacto no grupo etário referido, ocorre em simultâneo com inúmeras outras
doenças neurodegenerativas, como a doença de Alzheimer. Atualmente, o diagnóstico de AAC
realiza-se quer em post-mortem, quer em pacientes vivos. No entanto, o diagnóstico em vida
é conseguido por meio de biópsias de tecidos cerebrais, sendo um método invasivo, o que
dificulta a intervenção clínica. Deste modo, torna-se imperativa a procura de alternativas
fiáveis e não invasivas em vida para auxiliar o diagnóstico da doença e permitir a melhoria da
qualidade de vida do paciente. Perante os progressos na área da tecnologia e medicina, esta
dissertação propõe o estudo da aplicação de algoritmos de Machine Learning (ML) para
revelar possíveis biomarcadores para auxiliar o diagnóstico e permitir uma intervenção
precoce. Deste modo, foram testados três cenários distintos: a classificação de quatro
doenças neurodegenerativas com dados anotados obtidos a partir de métricas visuais de
avaliação da atrofia, idade e sexo; a classificação das doenças com dados gerados a partir de
métodos radiómicos; e uma combinação das duas abordagens anteriores.
Neste documento apresenta-se e discute-se os resultados obtidos com a aplicação de quatro
diferentes algoritmos de ML que visam a deteção automática da doença associada à imagem
testada. Adicionalmente, é feita uma análise crítica de quais as características mais relevantes
que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que
através de aplicação de metodologias automáticas é possível o auxílio ao diagnostico médico
por especialistas e, no limite, a realização de diagnostico automático com elevada precisão.
Finalmente, são apresentadas possíveis alternativas de trabalho futuro para que os resultados
possam ser aperfeiçoados, como por exemplo, a segmentação das regiões de interesse, i.e.,
identificação das lesões, aquando da anotação por especialistas. Mediante a inclusão dessa
segmentação, uma vez que será mais especifica, os resultados serão, por sua vez,
aprimorados
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