1,123 research outputs found
Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.
Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care
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Dynamic low-level context for the detection of mild traumatic brain injury.
Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
-weighted and -weighted contrasts with only -weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap), with a fast run time~( 45
seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev
Deteção automática de lesões de esclerose múltipla em imagens de ressonância magnética cerebral utilizando BIANCA
The aim of this work was to design and optimize a workflow to apply the
Machine Learning classifier BIANCA (Brain Intensity AbNormalities
Classification Algorithm) to detect lesions characterized by white matter T2
hyperintensity in clinical Magnetic Resonance Multiple Sclerosis datasets.
The designed pipeline includes pre-processing, lesion identification and
optimization of BIANCA options.
The classifier has been trained and tuned on 15 cases making up the training
dataset of the MICCAI 2016 (Medical Image Computing and Computer
Assisted Interventions) challenge and then tested on 30 cases from the Lesjak
et al. public dataset.
The results obtained are in good agreement with those reported by the 13
teams concluding the MICCAI 2016 challenge, thus confirming that this
algorithm can be a reliable tool to detect and classify Multiple Sclerosis lesions
in Magnetic Resonance studies.Este trabalho teve como objetivo a conceção e otimização de um procedimento
para aplicação de um algoritmo de Machine Learning, o classificador BIANCA
(Brain Intensity AbNormalities Classification Algorithm), para deteção de lesões
caracterizadas por hiperintensidade em T2 da matéria branca em estudos
clÃnicos de Esclerose Múltipla por Ressonância Magnética.
O procedimento concebido inclui pré-processamento, identificação das lesões
e otimização dos parâmetros do algoritmo BIANCA.
O classificador foi treinado e afinado utilizando os 15 casos clÃnicos que
constituÃam o conjunto de treino do desafio MICCAI 2016 (Medical Image
Computing and Computer Assisted Interventions) e posteriormente testado em
30 casos clÃnicos de uma base de dados pública (Lesjak et al.).
Os resultados obtidos são em concordância com os alcançados pelas 13
equipas que concluÃram o desafio MICCAI 2016, confirmando que este
algoritmo pode ser uma ferramenta válida para a deteção e classificação de
lesões de Esclerose Múltipla em estudos de Ressonância Magnética.Mestrado em Tecnologias da Imagem Médic
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