174 research outputs found
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
clĂnicos de Esclerose MĂşltipla por Ressonância MagnĂ©tica.
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 clĂnicos que
constituĂam o conjunto de treino do desafio MICCAI 2016 (Medical Image
Computing and Computer Assisted Interventions) e posteriormente testado em
30 casos clĂnicos de uma base de dados pĂşblica (Lesjak et al.).
Os resultados obtidos são em concordância com os alcançados pelas 13
equipas que concluĂram 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
Translation of quantitative MRI analysis tools for clinical neuroradiology application
Quantification of imaging features can assist radiologists by reducing subjectivity, aiding detection of subtle pathology, and increasing reporting consistency. Translation of quantitative image analysis techniques to clinical use is currently uncommon and challenging. This thesis explores translation of quantitative imaging support tools for clinical neuroradiology use. I have proposed a translational framework for development of quantitative imaging tools, using dementia as an exemplar application. This framework emphasises the importance of clinical validation, which is not currently prioritised. Aspects of the framework were then applied to four disease areas: hippocampal sclerosis (HS) as a cause of epilepsy; dementia; multiple sclerosis (MS) and gliomas. A clinical validation study for an HS quantitative report showed that when image interpreters used the report, they were more accurate and confident in their assessments, particularly for challenging bilateral cases. A similar clinical validation study for a dementia reporting tool found improved sensitivity for all image interpreters and increased assessment accuracy for consultant radiologists. These studies indicated benefits from quantitative reports that contextualise a patient’s results with appropriate normative reference data. For MS, I addressed a technical translational challenge by applying lesion and brain quantification tools to standard clinical image acquisitions which do not include a conventional T1-weighted sequence. Results were consistent with those from conventional sequence inputs and therefore I pursued this concept to establish a clinically applicable normative reference dataset for development of a quantitative reporting tool for clinical use. I focused on current radiology reporting of gliomas to establish which features are commonly missed and may be important for clinical management decisions. This informs both the potential utility of a quantitative report for gliomas and its design and content. I have identified numerous translational challenges for quantitative reporting and explored aspects of how to address these for several applications across clinical neuroradiology
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
In recent years, several convolutional neural network (CNN) methods have been
proposed for the automated white matter lesion segmentation of multiple
sclerosis (MS) patient images, due to their superior performance compared with
those of other state-of-the-art methods. However, the accuracies of CNN methods
tend to decrease significantly when evaluated on different image domains
compared with those used for training, which demonstrates the lack of
adaptability of CNNs to unseen imaging data. In this study, we analyzed the
effect of intensity domain adaptation on our recently proposed CNN-based MS
lesion segmentation method. Given a source model trained on two public MS
datasets, we investigated the transferability of the CNN model when applied to
other MRI scanners and protocols, evaluating the minimum number of annotated
images needed from the new domain and the minimum number of layers needed to
re-train to obtain comparable accuracy. Our analysis comprised MS patient data
from both a clinical center and the public ISBI2015 challenge database, which
permitted us to compare the domain adaptation capability of our model to that
of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot
domain adaptation model trained using only a single image showed a performance
similar to that of other CNN methods that were fully trained using the entire
available training set, yielding a comparable human expert rater performance.
We believe that our experiments will encourage the MS community to incorporate
its use in different clinical settings with reduced amounts of annotated data.
This approach could be meaningful not only in terms of the accuracy in
delineating MS lesions but also in the related reductions in time and economic
costs derived from manual lesion labeling
MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure
International audienceThis proceedings book gathers methodological papers describing the segmenta-tion methods evaluated at the second MICCAI Challenge on Multiple Sclerosisnew lesions segmentation challenge using a data management and processinginfrastructure. This challenge took place as part of an effort of the OFSEP1(French registry on multiple sclerosis aiming at gathering, for research purposes,imaging data, clinical data and biological samples from the French populationof multiple sclerosis subjects) and FLI2(France Life Imaging, devoted to setupa national distributed e-infrastructure to manage and process medical imagingdata). These joint efforts are directed towards automatic segmentation of MRIscans of MS patients to help clinicians in their daily practice. This challengetook place at the MICCAI 2021 conference, on September 23rd 2021.More precisely, the problem addressed in this challenge is as follows. Con-ventional MRI is widely used for disease diagnosis, patient follow-up, monitoringof therapies, and more generally for the understanding of the natural history ofMS. A growing literature is interested in the delineation of new MS lesions onT2/FLAIR by comparing one time point to another. This marker is even morecrucial than the total number and volume of lesions as the accumulation of newlesions allows clinicians to know if a given anti-inflammatory DMD (disease mod-ifying drug) works for the patient. The only indicator of drug efficacy is indeedthe absence of new T2 lesions within the central nervous system. Performingthis new lesions count by hand is however a very complex and time consumingtask. Automating the detection of these new lesions would therefore be a majoradvance for evaluating the patient disease activity.Based on the success of the first MSSEG challenge, we have organized aMICCAI sponsored online challenge, this time on new MS lesions detection3.This challenge has allowed to 1) estimate the progress performed during the2016 - 2021 period, 2) extend the number of patients, and 3) focus on the newlesions crucial clinical marker. We have performed the evaluation task on a largedatabase (100 patients, each with two time points) compiled from the OFSEPcohort with 3D FLAIR images from different centers and scanners. As in ourprevious challenge, we have conducted the evaluation on a dedicated platform(FLI-IAM) to automate the evaluation and remove the potential biases due tochallengers seeing the images on which the evaluation is made
Boosting multiple sclerosis lesion segmentation through attention mechanism
Magnetic resonance imaging is a fundamental tool to reach a diagnosis of
multiple sclerosis and monitoring its progression. Although several attempts
have been made to segment multiple sclerosis lesions using artificial
intelligence, fully automated analysis is not yet available. State-of-the-art
methods rely on slight variations in segmentation architectures (e.g. U-Net,
etc.). However, recent research has demonstrated how exploiting temporal-aware
features and attention mechanisms can provide a significant boost to
traditional architectures. This paper proposes a framework that exploits an
augmented U-Net architecture with a convolutional long short-term memory layer
and attention mechanism which is able to segment and quantify multiple
sclerosis lesions detected in magnetic resonance images. Quantitative and
qualitative evaluation on challenging examples demonstrated how the method
outperforms previous state-of-the-art approaches, reporting an overall Dice
score of 89% and also demonstrating robustness and generalization ability on
never seen new test samples of a new dedicated under construction dataset
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation
(SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of
automatically identifying pathologies in brain images. Our work challenges the
effectiveness of current Machine Learning (ML) approaches in this application
domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR)
MR scans provides better anomaly segmentation maps than several different
ML-based anomaly detection models. Specifically, our method achieves better
Dice similarity coefficients and Precision-Recall curves than the competitors
on various popular evaluation data sets for the segmentation of tumors and
multiple sclerosis lesions.Comment: 10 pages, 4 figures, accepted to the MICCAI 2021 BrainLes Worksho
Segmentation and quantification of spinal cord gray matter–white matter structures in magnetic resonance images
This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers.
Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations.
We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences.
On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions.
From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background
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