Personalized Pancreatic Tumor Growth Prediction via Group Learning

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method

Tumor Growth Parameters Estimation and Source Localization From a Unique Time Point: Application to Low-grade Gliomas

International audienceCoupling time series of MR Images with reaction-di usion-based models has provided interesting ways to better understand the proliferative-invasive as- pect of glial cells in tumors. In this paper, we address a di erent formulation of the inverse problem: from a single time point image of a non-swollen brain tumor, estimate the tumor source location and the di usivity ratio between white and grey matter, while exploring the possibility to predict the further extent of the observed tumor at later time points in low-grade gliomas. The synthetic and clinical results show the stability of the located source and its varying distance from the tumor barycenter and how the estimated ratio controls the spikiness of the tumor

Dynamic Model of Communicating Hydrocephalus for Surgery Simulation

We propose a dynamic model of cerebrospinal fluid circulation and intracranial pressure regulation. In this model, we investigate the coupling of biological parameters with a 3D model, to improve the mechanical behavior of the brain in surgical simulators. The model was assessed by comparing the simulated ventricular enlargement evolution with a patient case study of communicating hydrocephalus. In our model, cerebro-spinal fluid production-resorption system is coupled with a 3D representation of the brain parenchyma. We introduce a new bi-phasic model of the brain tissue allowing for fluid exchange between the brain extracellular space and the venous system. The time evolution of ventricular pressure has been recorded on a symptomatic patient after closing the ventricular shunt. A finite element model has been built based on a CT scan of this patient, and quantitative comparisons between measures and simulated data are proposed

Personalization of Reaction-Diffusion Tumor Growth Models in MR Images: Application to Brain Gliomas Characterization and Radiotherapy Planning

International audienceReaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images, specifically anatomical and diffusion images, in their formulation. On the other hand, the adaptation of the general model to the specific patient cases has not been studied thoroughly yet. In this chapter we address this adaptation. This chapter is a short summary of the articles (Konukoglu 2009a), (Konukoglu 2009b) and the thesis (Konukoglu 2009c) that we have submitted recently. In the first part, we describe a parameter estimation method for reaction-diffusion tumor growth models using time series of medical (Magnetic Resonance) images. This method estimates the patient specific parameters of the model using the images of the patient taken at different successive time instances. In the second part of the chapter we focus on an application of the personalized models aimed to improve the tumor targeting in radiation therapy. Specifically we address the problem of limited visualization of medical images. We describe a method for extrapolating the invisible infiltration margins of gliomas in the MR images and the usage of these margins in constructing irradiation margins taking into account the growth dynamics of the tumor. Finally for both parts we show preliminary results demonstrating the power and the potential benefits of the personalizatio

Dynamic Model of Communicating Hydrocephalus for Surgery Simulation

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Biomechanical Simulation of Electrode Migration for Deep Brain Stimulation

International audienceDeep Brain Stimulation is a modern surgical technique for treating patients who suffer from affective or motion disorders such as Parkinson's disease. The efficiency of the procedure relies heavily on the accuracy of the placement of a micro-electrode which sends electrical pulses to a specific part of the brain that controls motion and affective symptoms. However, targeting this small anatomical structure is rendered difficult due to a series of brain shifts that take place during and after the procedure. This paper introduces a biomechanical simulation of the intra and postoperative stages of the procedure in order to determine lead deformation and electrode migration due to brain shift. To achieve this goal, we propose a global approach, which accounts for brain deformation but also for the numerous interactions that take place during the procedure (contacts between the brain and the inner part of the skull and falx cerebri, effect of the cerebro-spinal fluid, and biomechanical interactions between the brain and the electrodes and cannula used during the procedure). Preliminary results show a good correlation between our simulations and various results reported in the literature

Asclepios: a Research Project-Team at INRIA for the Analysis and Simulation of Biomedical Images

International audienceAsclepios1 is the name of a research project-team o cially launched on November 1st, 2005 at INRIA Sophia-Antipolis, to study the Analysis and Simulation of Biological and Medical Images. This research project-team follows a previous one, called Epidaure, initially dedicated to Medical Imaging and Robotics research. These two project teams were strongly supported by Gilles Kahn, who used to have regular scienti c in- teractions with their members. More generally, Gilles Kahn had a unique vision of the growing importance of the interaction of the Information Technologies and Sciences with the Biological and Medical world. He was one of the originators of the creation of a speci c BIO theme among the main INRIA research directions, which now regroups 16 di fferent research teams including Asclepios, whose research objectives are described and illustrated in this article

Reconstruction 3D des structures anatomiques des membres inférieurs

National audienceDans cet article, nous nous intéressons à la modélisation des structures anatomiques des membres inférieurs telles que les os, les muscles et les tendons. La méthode proposée commence par une acquisition d'images IRM durant laquelle les membres inférieurs d'un sujet sont scannés. Des modèles 3D sont ensuite générés après une segmentation manuelle des structures anatomiques. Cependant, la surface des modèles générés n'est pas lisse. De plus, les modèles ne sont pas attachés alors qu'ils devraient l'être anatomiquement. Nous décrivons donc les différentes étapes pour contraindre les modèles à être corrects au niveau anatomique et nous discutons de leur validation. L'objectif de cette méthode est de pouvoir réutiliser ces modèles dans des méthodes de segmentation automatique

Vers un patient numérique personnalisé pour le diagnostic et la thérapie guidés par l'image [Towards a personalized digital patient for diagnosis and therapy guided by image].

National audienceRecent advances in computer science and medical imaging allow the design of new computational models of the patient which are used to assist physicians. These models, whose parameters are optimized to fit in vivo acquired images, from cells to an entire body, are designed to better quantify the observations (computer aided diagnosis), to simulate the evolution of a pathology (computer aided prognosis), to plan and simulate an intervention to optimize its effects (computer aided therapy), therefore addressing some of the major challenges of medicine of 21(st) century

Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room