32 research outputs found
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
SoftSeg: Advantages of soft versus binary training for image segmentation
Most image segmentation algorithms are trained on binary masks formulated as
a classification task per pixel. However, in applications such as medical
imaging, this "black-and-white" approach is too constraining because the
contrast between two tissues is often ill-defined, i.e., the voxels located on
objects' edges contain a mixture of tissues. Consequently, assigning a single
"hard" label can result in a detrimental approximation. Instead, a soft
prediction containing non-binary values would overcome that limitation. We
introduce SoftSeg, a deep learning training approach that takes advantage of
soft ground truth labels, and is not bound to binary predictions. SoftSeg aims
at solving a regression instead of a classification problem. This is achieved
by using (i) no binarization after preprocessing and data augmentation, (ii) a
normalized ReLU final activation layer (instead of sigmoid), and (iii) a
regression loss function (instead of the traditional Dice loss). We assess the
impact of these three features on three open-source MRI segmentation datasets
from the spinal cord gray matter, the multiple sclerosis brain lesion, and the
multimodal brain tumor segmentation challenges. Across multiple
cross-validation iterations, SoftSeg outperformed the conventional approach,
leading to an increase in Dice score of 2.0% on the gray matter dataset
(p=0.001), 3.3% for the MS lesions, and 6.5% for the brain tumors. SoftSeg
produces consistent soft predictions at tissues' interfaces and shows an
increased sensitivity for small objects. The richness of soft labels could
represent the inter-expert variability, the partial volume effect, and
complement the model uncertainty estimation. The developed training pipeline
can easily be incorporated into most of the existing deep learning
architectures. It is already implemented in the freely-available deep learning
toolbox ivadomed (https://ivadomed.org)
Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning
Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of three-dimensional structures is time consuming, tedious and prone to intra- and inter-rater variability, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enhanced T1-weighted and T2-weighted MRI scans with cervical, thoracic, and/or lumbar coverage. The dataset includes the three most common intramedullary spinal cord tumor types: astrocytomas, ependymomas, and hemangioblastomas. The proposed approach is a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The model first finds the spinal cord and generates bounding box coordinates. The images are cropped according to this output, leading to a reduced field of view, which mitigates class imbalance. The tumor is then segmented. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 ± 1.5% of Dice score and the segmentation of tumors alone reached 61.8 ± 4.0% Dice score. The true positive detection rate was above 87% for tumor, edema, and cavity. To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation. The multiclass segmentation pipeline is available in the Spinal Cord Toolbox (https://spinalcordtoolbox.com/). It can be run with custom data on a regular computer within seconds
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
2D multi-class model for gray and white matter segmentation of the cervical spinal cord at 7T
8 pages, 5 figuresThe spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurological disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired. While automated methods for WM/GM segmentation are now largely available, these techniques, developed for conventional systems (3T or lower) do not necessarily perform well on 7T MRI data, which feature finer details, contrasts, but also different artifacts or signal dropout. The primary goal of this study is thus to propose a new deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images. The second objective is to highlight the relevance of implementing a specific data augmentation (DA) strategy, in particular to generate a generic model that could be used for multi-center studies at 7T
Benefits of linear conditioning for segmentation using metadata
Medical images are often accompanied by metadata describing the image
(vendor, acquisition parameters) and the patient (disease type or severity,
demographics, genomics). This metadata is usually disregarded by image
segmentation methods. In this work, we adapt a linear conditioning method
called FiLM (Feature-wise Linear Modulation) for image segmentation tasks. This
FiLM adaptation enables integrating metadata into segmentation models for
better performance. We observed an average Dice score increase of 5.1% on
spinal cord tumor segmentation when incorporating the tumor type with FiLM. The
metadata modulates the segmentation process through low-cost affine
transformations applied on feature maps which can be included in any neural
network's architecture. Additionally, we assess the relevance of segmentation
FiLM layers for tackling common challenges in medical imaging: training with
limited or unbalanced number of annotated data, multi-class training with
missing segmentations, and model adaptation to multiple tasks. Our results
demonstrated the following benefits of FiLM for segmentation: FiLMed U-Net was
robust to missing labels and reached higher Dice scores with few labels (up to
16.7%) compared to single-task U-Net. The code is open-source and available at
www.ivadomed.org