421 research outputs found
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of
cancer-related death in children. The five-year survival rate for high-grade
gliomas in children is less than 20\%. Due to their rarity, the diagnosis of
these entities is often delayed, their treatment is mainly based on historic
treatment concepts, and clinical trials require multi-institutional
collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a
landmark community benchmark event with a successful history of 12 years of
resource creation for the segmentation and analysis of adult glioma. Here we
present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which
represents the first BraTS challenge focused on pediatric brain tumors with
data acquired across multiple international consortia dedicated to pediatric
neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on
benchmarking the development of volumentric segmentation algorithms for
pediatric brain glioma through standardized quantitative performance evaluation
metrics utilized across the BraTS 2023 cluster of challenges. Models gaining
knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training
data will be evaluated on separate validation and unseen test mpMRI dataof
high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023
challenge brings together clinicians and AI/imaging scientists to lead to
faster development of automated segmentation techniques that could benefit
clinical trials, and ultimately the care of children with brain tumors
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Functional Magnetic Resonance Imaging
"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states
Machine Learning towards General Medical Image Segmentation
The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency
Automatic Segmentation of Intramedullary Multiple Sclerosis Lesions
Contexte: La moelle Ă©piniĂšre est un composant essentiel du systĂšme nerveux central. Elle contient des neurones responsables dâimportantes fonctionnalitĂ©s et assure la transmission dâinformations motrices et sensorielles entre le cerveau et le systĂšme nerveux pĂ©riphĂ©rique. Un endommagement de la moelle Ă©piniĂšre, causĂ© par un choc ou une maladie neurodĂ©gĂ©nĂ©rative, peut mener Ă un sĂ©rieux handicap, pouvant entraĂźner des incapacitĂ©s fonctionnelles, de la paralysie et/ou de la douleur. Chez les patients atteints de sclĂ©rose en plaques (SEP), la moelle Ă©piniĂšre est frĂ©quemment affectĂ©e par de lâatrophie et/ou des lĂ©sions. Lâimagerie par rĂ©sonance magnĂ©tique (IRM) conventionnelle est largement utilisĂ©e par des chercheurs et des cliniciens pour Ă©valuer et caractĂ©riser, de façon non-invasive, des altĂ©rations micro-structurelles. Une Ă©valuation quantitative des atteintes structurelles portĂ©es Ă la moelle Ă©piniĂšre (e.g. sĂ©vĂ©ritĂ© de lâatrophie, extension des lĂ©sions) est essentielle pour le diagnostic, le pronostic et la supervision sur le long terme de maladies, telles que la SEP. De plus, le dĂ©veloppement de biomarqueurs impartiaux est indispensable pour Ă©valuer lâeffet de nouveaux traitements thĂ©rapeutiques. La segmentation de la moelle Ă©piniĂšre et des lĂ©sions intramĂ©dullaires de SEP sont, par consĂ©quent, pertinentes dâun point de vue clinique, aussi bien quâune Ă©tape nĂ©cessaire vers lâinterprĂ©tation dâimages RM multiparamĂ©triques. Cependant, la segmentation manuelle est une tĂąche extrĂȘmement chronophage, fastidieuse et sujette Ă des variations inter- et intra-expert. Il y a par consĂ©quent un besoin dâautomatiser les mĂ©thodes de segmentations, ce qui pourrait faciliter lâefficacitĂ© procĂ©dures dâanalyses. La segmentation automatique de lĂ©sions est compliquĂ© pour plusieurs raisons: (i) la variabilitĂ© des lĂ©sions en termes de forme, taille et position, (ii) les contours des lĂ©sions sont la plupart du temps difficilement discernables, (iii) lâintensitĂ© des lĂ©sions sur des images MR sont similaires Ă celles de structures visiblement saines. En plus de cela, rĂ©aliser une segmentation rigoureuse sur lâensemble dâune base de donnĂ©es multi-centrique dâIRM est rendue difficile par lâimportante variabilitĂ© des protocoles dâacquisition (e.g. rĂ©solution, orientation, champ de vue de lâimage). MalgrĂ© de considĂ©rables rĂ©cents dĂ©veloppements dans le traitement dâimages MR de moelle Ă©piniĂšre, il nây a toujours pas de mĂ©thode disponible pouvant fournir une segmentation rigoureuse et fiable de la moelle Ă©piniĂšre pour un large spectre de pathologies et de protocoles dâacquisition. Concernant les lĂ©sions intramĂ©dullaires, une recherche approfondie dans la littĂ©rature nâa pas pu fournir une mĂ©thode disponible de segmentation automatique.
Objectif: Développer un systÚme complÚtement automatique pour segmenter la moelle épiniÚre et les lésions intramédullaires sur des IRM conventionnelles humaines.
MĂ©thode: Lâapproche prĂ©sentĂ©e est basĂ©e de deux rĂ©seaux de neurones Ă convolution mis en cascade. La mĂ©thode a Ă©tĂ© pensĂ©e pour faire face aux principaux obstacles que prĂ©sentent les donnĂ©es IRM de moelle Ă©piniĂšre. Le procĂ©dĂ© de segmentation a Ă©tĂ© entrainĂ© et validĂ© sur une base de donnĂ©es privĂ©e composĂ©e de 1943 images, acquises dans 30 diffĂ©rents centres avec des protocoles hĂ©tĂ©rogĂšnes. Les sujets scannĂ©s comportent 459 sujets sains, 471 patients SEP et 112 avec dâautres pathologies affectant la moelle Ă©piniĂšre. Le module de segmentation de la moelle Ă©piniĂšre a Ă©tĂ© comparĂ© Ă une mĂ©thode existante reconnue par la communautĂ©, PropSeg.
RĂ©sultats: Lâapproche basĂ©e sur les rĂ©seaux de neurones Ă convolution a fourni de meilleurs rĂ©sultats que PropSeg, atteignant un Dice mĂ©dian (intervalle inter-quartiles) de 94.6 (4.6) vs. 87.9 (18.3) %. Pour les lĂ©sions, notre segmentation automatique a permis d'obtenir un Dice de 60.0 (21.4) % en le comparant Ă la segmentation manuelle, un ratio de vrai positifs de 83 (34) %, et une prĂ©cision de 77 (44) %.
Conclusion: Une mĂ©thode complĂštement automatique et innovante pour segmenter la moelle Ă©piniĂšre et les lĂ©sions SEP intramĂ©dullaires sur des donnĂ©es IRM a Ă©tĂ© conçue durant ce projet de maĂźtrise. La mĂ©thode a Ă©tĂ© abondamment validĂ©e sur une base de donnĂ©es clinique. La robustesse de la mĂ©thode de segmentation de moelle Ă©piniĂšre a Ă©tĂ© dĂ©montrĂ©e, mĂȘme sur des cas pathologiques. Concernant la segmentation des lĂ©sions, les rĂ©sultats sont encourageants, malgrĂ© un taux de faux positifs relativement Ă©levĂ©. Je crois en lâimpact que peut potentiellement avoir ces outils pour la communautĂ© de chercheurs. Dans cette optique, les mĂ©thodes ont Ă©tĂ© intĂ©grĂ©es et documentĂ©es dans un logiciel en accĂšs-ouvert, la âSpinal Cord Toolboxâ. Certains des outils dĂ©veloppĂ©s pendant ce projet de MaĂźtrise sont dĂ©jĂ utilisĂ©s par des analyses dâĂ©tudes cliniques, portant sur des patients SEP et sclĂ©rose latĂ©rale amyotrophique.----------ABSTRACT
Context: The spinal cord is a key component of the central nervous system, which contains neurons responsible for complex functions, and ensures the conduction of motor and sensory information between the brain and the peripheral nervous system. Damage to the spinal cord, through trauma or neurodegenerative diseases, can lead to severe impairment, including functional disabilities, paralysis and/or pain. In multiple sclerosis (MS) patients, the spinal cord is frequently affected by atrophy and/or lesions. Conventional magnetic resonance imaging (MRI) is widely used by researchers and clinicians to non-invasively assess and characterize spinal cord microstructural changes. Quantitative assessment of the structural damage to the spinal cord (e.g. atrophy severity, lesion extent) is essential for the diagnosis, prognosis and longitudinal monitoring of diseases, such as MS. Furthermore, the development of objective biomarkers is essential to evaluate the effect of new therapeutic treatments. Spinal cord and intramedullary MS lesions segmentation is consequently clinically relevant, as well as a necessary step towards the interpretation of multi-parametric MR images. However, manual segmentation is highly time-consuming, tedious and prone to intra- and inter-rater variability. There is therefore a need for automated segmentation methods to facilitate the efficiency of analysis pipelines. Automatic lesion segmentation is challenging for various reasons: (i) lesion variability in terms of shape, size and location, (ii) lesion boundaries are most of the time not well defined, (iii) lesion intensities on MR data are confounding with those of normal-appearing structures. Moreover, achieving robust segmentation across multi-center MRI data is challenging because of the broad variability of data features (e.g. resolution, orientation, field of view). Despite recent substantial developments in spinal cord MRI processing, there is still no method available that can yield robust and reliable spinal cord segmentation across the very diverse spinal pathologies and data features. Regarding the intramedullary lesions, a thorough search of the relevant literature did not yield available method of automatic segmentation.
Goal: To develop a fully-automatic framework for segmenting the spinal cord and intramedullary MS lesions from conventional human MRI data.
Method: The presented approach is based on a cascade of two Convolutional Neural Networks (CNN). The method has been designed to face the main challenges of âreal worldâ spinal cord MRI data. It was trained and validated on a private dataset made up of 1943 MR volumes, acquired in different 30 sites with heterogeneous acquisition protocols. Scanned subjects involve 459 healthy controls, 471 MS patients and 112 with other spinal pathologies. The proposed spinal cord segmentation method was compared to a state-of-the-art spinal cord segmentation method, PropSeg.
Results: The CNN-based approach achieved better results than PropSeg, yielding a median (interquartile range) Dice of 94.6 (4.6) vs. 87.9 (18.3) % when compared to the manual segmentation. For the lesion segmentation task, our method provided a median Dice-overlap with the manual segmentation of 60.0 (21.4) %, a lesion-based true positive rate of 83 (34) % and a lesion-based precision de 77 (44) %.
Conclusion: An original fully-automatic method to segment the spinal cord and intramedullary MS lesions on MRI data has been devised during this Masterâs project. The method was validated extensively against a clinical dataset. The robustness of the spinal cord segmentation has been demonstrated, even on challenging pathological cases. Regarding the lesion segmentation, the results are encouraging despite the fairly high false positive rate. I believe in the potential value of these developed tools for the research community. In this vein, the methods are integrated and documented into an open-source software, the Spinal Cord Toolbox. Some of the tools developed during this Masterâs project are already integrated into automated analysis pipelines of clinical studies, including MS and Amyotrophic Lateral Sclerosis patients
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
The investigation of early MRI in diagnosis and prognosis in patients presenting with a clinically isolated syndrome characteristic of demyelination
This thesis explores the use of early MRI in prognosis and diagnosis in patients presenting with a clinically isolated syndrome (CIS) characteristic of demyelination. This has been investigated in a cohort recruited within 3 months of CIS onset between 1995 and 2004 and followed up clinically and with MRI (planned at 3 months, 1,3 and 5 years).
Current MRI criteria are highly specific for the development of clinically definite multiple sclerosis (CDMS) but have limited sensitivity and are complex. Presented is the evaluation of simplified MRI criteria in my London CIS cohort and in a multicentre CIS cohort. Results from the presented studies show that the MRI criteria can be simplified (dissemination in space: 2 or more lesions in separate but characteristic locations, dissemination in time: an early new T2 lesion) and still maintain high specificity, with improved sensitivity and accuracy.
The prognostic role of early MRI was investigated in the optic neuritis (ON) subgroup, as 80% of my cohort presented with ON and some studies have suggested that such a presentation is associated with more benign disease. Whereas baseline lesion number significantly predicted conversion to CDMS and increased disability at 5 years, other MRI parameters, namely baseline lesion location (periventricular lesions increasing the hazard of CDMS and spinal cord and infratentorial lesions increasing the odds of greater disability at 5 years) and lesion activity (new T2 lesion at 3 month follow-up), were stronger predictors. No non-conventional MRI parameters (spectroscopy, magnetisation transfer ratio or atrophy measures) had a significant prognostic role.
Overall early MRI findings can aid diagnosis and help identify the CIS patients at greatest risk of conversion to CDMS and subsequent disability, which in turn can help direct treatment and clinical follow-up in specialist MS clinics
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