70 research outputs found

    Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy

    Get PDF
    International audiencePlanning of radiotherapy involves accurate segmentation of a large number of organs at risk (OAR), i.e., organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of OAR inside the head, from magnetic resonance images (MRIs). Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem, and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and nonexclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step. In particular, we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered OAR, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs were evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 to 0.7 mm across different organs. A vast majority (96%) of the produced segmentations were found acceptable for radiotherapy planning

    Brain Tumor Growth Simulation

    Get PDF
    In the present report, we propose a new model to simulate the growth of glioblastomas multiforma (GBM), the most aggressive glial tumors. Because the GBM shows a preferential growth in the white fibers and have a distinct invasion speed with respect to the nature of the invaded tissue, we rely on an anatomical atlas to introduce this information into the model. This atlas includes a white fibers diffusion tensor information and the delineation of cerebral structures having a distinct response to the tumor aggression. We use the finite element method (FEM) to simulate both the invasion of the GBM in the brain parenchyma and its mechanical interaction (mass effect) with the invaded structures. The former effect is modeled with either a reaction-diffusion or a Gompertz equation depending on the considered tissue, while the latter is based on a linear elastic brain constitutive equation. In addition, we propose a new coupling equation taking into account the mechanical influence of the tumor cells on the invaded tissues. This tumor growth model is assessed by comparing the \textit{in-silico} GBM growth with the real GBM growth observed between two magnetic resonance images (MRIs) of a patient acquired with six months difference. The quality of the results shows the feasibility of modeling the complex behavior of brain tumors and will justify a further validation of this new conceptual approach

    Early Toxicities After High Dose Rate Proton Therapy in Cancer Treatments

    Get PDF
    Background: The conventional dose rate of radiation therapy is 0.01-0.05 Gy per second. According to preclinical studies, an increased dose rate may offer similar anti-tumoral effect while dramatically improving normal tissue protection. This study aims at evaluating the early toxicities for patients irradiated with high dose rate pulsed proton therapy (PT). Materials and methods: A single institution retrospective chart review was performed for patients treated with high dose rate (10 Gy per second) pulsed proton therapy, from September 2016 to April 2020. This included both benign and malignant tumors with ≄3 months follow-up, evaluated for acute (≀2 months) and subacute (>2 months) toxicity after the completion of PT. Results: There were 127 patients identified, with a median follow up of 14.8 months (3-42.9 months). The median age was 55 years (1.6-89). The cohort most commonly consisted of benign disease (55.1%), cranial targets (95.1%), and were treated with surgery prior to PT (56.7%). There was a median total PT dose of 56 Gy (30-74 Gy), dose per fraction of 2 Gy (1-3 Gy), and CTV size of 47.6 ml (5.6-2,106.1 ml). Maximum acute grade ≄2 toxicity were observed in 49 (38.6%) patients, of which 8 (6.3%) experienced grade 3 toxicity. No acute grade 4 or 5 toxicity was observed. Maximum subacute grade 2, 3, and 4 toxicity were discovered in 25 (19.7%), 12 (9.4%), and 1 (0.8%) patient(s), respectively. Conclusion: In this cohort, utilizing high dose rate proton therapy (10 Gy per second) did not result in a major decrease in acute and subacute toxicity. Longer follow-up and comparative studies with conventional dose rate are required to evaluate whether this approach offers a toxicity benefit

    Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth using a Mechanically-coupled Reaction-Diffusion Model

    Get PDF
    Glioblastoma (GBM), the most frequent malignant brain tumor in adults, is char- acterized by rapid growth and healthy tissue invasion. Long-term prognosis for GBM remains poor with median overall survival between 1 y to 2 y [15]. GBM presents with different growth phenotypes, ranging from invasive tumors without notable mass-effect to strongly displacing lesions. Biomechanical forces, such as those resulting from displacive tumor growth, shape the tumor environment and contribute to tumor progression [9]. We present an extended version of a mechanically–coupled reaction-diffusion model of brain tu- mor growth [1] that simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. This model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. We modified this model to account for structural tissue anisotropy which is known to affect the directionality of tumor cell migration and may influence the mechanical behavior of brain tissue. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatio-temporal evolution was simulated using the Finite-Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. We found the impact on tumor shape to be strongly location dependent and highest for tumors located in brain regions that are characterized by a single dominant white matter direction, such as the corpus callosum. However, despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding location and similar volume. This finding is in agreement with previous studies [17, 6] suggesting that anisotropic cell migration along white matter fiber tracks is not a major determinant of tumor shape in the setting of reaction-diffusion based tumor growth models and for most locations across the brain

    Mise en Ɠuvre et Ă©valuation d'outils de fusion d'image en radiothĂ©rapie

    No full text
    Le fichier contient l'intĂ©gralitĂ© de la thĂšse en français et en anglaisCancer is a major problem of public health. Treatment can be done in a general or loco-regional wa, in this last case medical images are important as they specify the localization of the tumour. The objective of the radiotherapy is to deliver a curative dose of radiation in the target volume while sparing the organs at risks (OAR). The determination of the accurate localization of the targets volume as well as OAR make it possible to define the balistic of irradiation beams. After the description of the principles of radiotherapy and cancers treatment, we specify the clinical stakes of ocular, cerebral and prostatic tumours.We present a state of the art of image matching, the various techniques reviewed with an aim of being didactic with respect to the medical community. The results of matching are presented within the framework of the planning of the cerebral and prostatic radiotherapy in order to specify the types of applicable matching in oncology and more particularly in radiotherapy. Then, we present the prospects for this type of application according to various anatomical areas. Applications of automatic segmentation and the evaluation of the results in the framework of brain tumour are described after a review of the various segmentation methods according to anatomical localizations.We will see an original application : the digital simulation of the virtual tumoral growth and the comparison with the real growth of a cerebral tumour presented by a patient.Lastly, we will expose the future developments possible of the tools for image processing in radiotherapy as well as the tracks of research to be explored in oncologyLe cancer est un problĂšme majeur de santĂ© publique. Les traitements peuvent ĂȘtre Ă  visĂ©e systĂ©mique ou loco-rĂ©gionale, dans ce dernier cas l'imagerie mĂ©dicale joue un rĂŽle important en permettant de prĂ©ciser la localisation de la tumeur. L'objectif de la radiothĂ©rapie est de dĂ©livrer une dose curatrice de radiation au volume cible tout en Ă©pargnant les organes Ă  risques (OAR) avoisinants. La dĂ©termination de la localisation prĂ©cise de ce volume cible ainsi que des OAR permet de dĂ©finir la position et la puissance des faisceaux d'irradiation. AprĂšs le rappel des principes de la radiothĂ©rapie et du traitement des cancers, nous prĂ©cisons les enjeux cliniques des tumeurs oculaires, cĂ©rĂ©brales et prostatiques.Nous effectuerons une mise au point sur le recalage d'images, les diffĂ©rentes techniques sont passĂ©es en revue dans le but d'ĂȘtre didactique vis-Ă -vis de la communautĂ© mĂ©dicale. Les rĂ©sultats du recalage sont prĂ©sentĂ©s dans le cadre de la planification de la radiothĂ©rapie cĂ©rĂ©brale et prostatique afin de prĂ©ciser les types de recalage applicables en oncologie et plus particuliĂšrement Ă  la radiothĂ©rapie.Ensuite, nous prĂ©sentons les perspectives de ce type d'application selon diffĂ©rentes rĂ©gions anatomiques. Les applications de la segmentation automatiques et l'Ă©valuation des rĂ©sultats dans le cadre des tumeur de l'encĂ©phale sont dĂ©crits aprĂšs avoir passĂ© en revue les diffĂ©rentes mĂ©thodes de segmentation utilisables selon les localisations anatomiques.Nous verrons une application originale : la simulation numĂ©rique de la croissance tumorale virtuelle et la comparaison avec la croissance rĂ©elle d'une tumeur cĂ©rĂ©brale prĂ©sentĂ©e par un patient.Enfin, nous exposerons les futurs dĂ©veloppements possibles des outils de traitement de l'image en radiothĂ©rapie ainsi que les pistes des recherche Ă  explorer en oncologie

    Mise en oeuvre et évaluation d'outils de fusion d'image en radiothérapie

    No full text
    Le cancer est un problĂšme majeur de santĂ© publique. Les traitements peuvent ĂȘtre Ă  visĂ©e systĂ©mique ou loco-rĂ©gionale, dans ce dernier cas l imagerie mĂ©dicale jour un rĂŽle important, permettant de prĂ©ciser la localisation de la tumeur. L objectif de la radiothĂ©rapie est de dĂ©livrer une dose curatrice de radiation au volume cible tout en Ă©pargnant les organes Ă  risques (OAR) avoisinants. La dĂ©termination de la localisation prĂ©cise de ce volume cible ainsi que des OAR permet de dĂ©finir la balistique des faisceaux d irradiation. AprĂšs le rappel des principes de la radiothĂ©rapie, nous prĂ©cisons les enjeux cliniques des tumeurs oculaires, cĂ©rĂ©brales et prostatiques. Les diffĂ©rentes techniques de recalage d images sont passĂ©es en revue dans le but d ĂȘtre didactique vis-Ă -vis de la communautĂ© mĂ©dicale. Les rĂ©sultats du recalage sont prĂ©sentĂ©s dans le cadre de la planification de la radiothĂ©rapie oculaire, cĂ©rĂ©brale et prostatique afin de prĂ©ciser les types de recalage applicable en oncologie. Nous prĂ©sentons les perspectives de ce type d application selon diffĂ©rentes rĂ©gions anatomiques ainsi que son intĂ©rĂȘt dans la segmentation automatique. Les applications de la segmentation automatique et l Ă©valuation des rĂ©sultats dans le cadre des tumeurs de l encĂ©phale sont dĂ©crits aprĂšs avoir passĂ© en revue les diffĂ©rentes mĂ©thodes de segmentation selon les localisations anatomiques. Une application originale est la simulation numĂ©rique de la croissance tumorale virtuelle qui est comparĂ©e avec la croissance rĂ©elle d une tumeur cĂ©rĂ©brale prĂ©sentĂ©e par un patient. Nous concluons avec les diffĂ©rents dĂ©veloppements de la fusion d image ainsi que les pistes de recherche devant ĂȘtre explorĂ©es.Cancer is a major problem of public health. Treatment can be done in a general or loco-regional way, in which case medical images are important as they specify the localization of the tumour. The objective of the radiotherapy is to deliver a curative dose of radiation in the target volume while sparing the organs at risks (OAR). The determination of the accurate localization of the targets volume as well as OAF makes it possible to define the ballistics of irradiation beams. After the description of the principles of radiotherapy and cancers treatment, we specify the clinical stakes of ocular, cerebral and prostatic tumours. We review the state of the art image matching algorithms, with a didactic purpose for the medical community. The results of image matching techniques are presented in the framework of cerebral and prostatic radiotherapy planning in order to determine the types of applicable method in oncology. Then, we present the prospects for these methods with respect to the anatomical localization and automatic segmentation. Applications of automatic segmentation and the evaluation of the results in the framework of brain tumour are described after a review of the various segmentation methods according to anatomical localizations. An original application is the digital simulation of the virtual tumoral growth and the comparison with the real growth of a cerebral tumour presented by a patient. We conclude with the future developments possible of the tools for image processing in radiotherapy as well as the tracks of research to be explored in oncology.NICE-BU Sciences (060882101) / SudocSOPHIA ANTIPOLIS-INRIA I3S (061522305) / SudocSudocFranceF
    • 

    corecore