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

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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

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Deformable Medical Image Registration: A Survey

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

/pdf/a-survey-of-mri-based-medical-image-analysis-for-brain-tumor-4mtl4fktmo.pdf

A survey of MRI-based medical image analysis for brain tumor studies

Summary: Increasing detection of unruptured intracranial aneurysms, catastrophic outcomes from subarachnoid hemorrhage, and risks and cost of treatment necessitate defining objective predictive parameters of aneurysm rupture risk. Image-based computational fluid dynamics models have suggested associations between hemodynamics and intracranial aneurysm rupture, albeit with conflicting findings regarding wall shear stress. We propose that the “high-versus-low wall shear stress” controversy is a manifestation of the complexity of aneurysm pathophysiology, and both high and low wall shear stress can drive intracranial aneurysm growth and rupture. Low wall shear stress and high oscillatory shear index trigger an inflammatory-cell-mediated pathway, which could be associated with the growth and rupture of large, atherosclerotic aneurysm phenotypes, while high wall shear stress combined with a positive wall shear stress gradient trigger a mural-cell-mediated pathway, which could be associated with the growth and rupture of small or secondary bleb aneurysm phenotypes. This hypothesis correlates disparate intracranial aneurysm pathophysiology with the results of computational fluid dynamics in search of more reliable risk predictors.

http://www.ajnr.org/content/ajnr/35/7/1254.full.pdf

High WSS or Low WSS? Complex Interactions of Hemodynamics with Intracranial Aneurysm Initiation, Growth, and Rupture: Toward a Unifying Hypothesis

Since its coming of age in the mid 1960s, continuum biomechanics has contributed much to our understanding of human health as well as to disease, injury, and their treatment. Nevertheless, biomechanics has yet to reach its full potential as a consistent contributor to the improvement of health–care delivery. Because of the inherent complexities of the microstructure and biomechanical behaviour of biological cells and tissues, there is a need for new theoretical frameworks to guide the design and interpretation of new classes of experiments. Because of continued advances in experimental technology, and the associated rapid increase in information on molecular and cellular contributions to behaviour at tissue and organ levels, there is a pressing need for mathematical models to synthesize and predict observations across multiple length– and time–scales. And because of the complex geometries and loading conditions, there is a need for new computational approaches to solve the boundaryand initial–value problems of clinical, industrial, and academic importance. Clearly, much remains to be done. The purpose of this paper is twofold: to review a few of the many achievements in the biomechanics of soft tissues and the tools that allowed them, but, more importantly, to identify some of the open problems that merit increased attention from those in applied mechanics, biomechanics, mathematics and mechanobiology.

Review Paper: Continuum biomechanics of soft biological tissues

Background and Purpose—We evaluated several hemodynamic parameters for the prediction of rupture in a data set of initially unruptured aneurysms, including aneurysms that ruptured during follow-up observation. Methods—Aneurysm geometry was extracted from CT angiographic images and analyzed using a mathematical formula for fluid flow under pulsatile blood flow conditions. Fifty side-wall internal carotid posterior communicating artery aneurysms and 50 middle cerebral artery bifurcation aneurysms of medium size were investigated for energy loss, pressure loss coefficient, wall shear stress, and oscillatory shear index. During follow-up observation, 6 internal carotid posterior communicating artery and 7 middle cerebral artery aneurysms ruptured (44 and 43 remained unruptured, respectively, with the same location and a similar size as the ruptured cases). Results—A significant difference in the minimum wall shear stress between aneurysms that ruptured and those that remained unruptured was noted only in inte...

Hemodynamic Differences Between Unruptured and Ruptured Intracranial Aneurysms During Observation

Brain biomechanics has been investigated for more than 30 years. In particular, finite element analyses and other powerful computational methods have long been used to provide quantitative results in the investigation of dynamic processes such as head trauma. Nevertheless, the potential of these methods to simulate and predict the outcome of quasi-static processes such as neurosurgical procedures and neuropathological processes has only recently been explored. Some inherent difficulties in modeling brain tissues, which have impeded progress, are discussed in this work. The behavior of viscoelastic and poroelastic constitutive models is compared in simple 1-D simulations using the ABAQUS finite element platform. In addition, the behaviors of quasi-static brain constitutive models that have recently been proposed are compared. We conclude that a compressible viscoelastic solid model may be the most appropriate for modeling neurosurgical procedures.

Brain mechanics For neurosurgery: modeling issues.

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.

A framework for predictive modeling of anatomical deformations

Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To simulate the widely varying behavior of brain tumors, the model is controlled by a number of parameters that are related to variables such as the bulk tumor location, size, mass-effect, and peri-tumor edema extent. Model predictions are compared to real brain tumor-induced deformations observed in serial-time MRI scans of a human subject and 3 canines with surgically transplanted gliomas. Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared.

/pdf/finite-element-modeling-of-brain-tumor-mass-effect-from-3d-4v5btpotey.pdf

Ashraf Mohamed

Papers

Hemodynamic Differences Between Unruptured and Ruptured Intracranial Aneurysms During Observation

Brain mechanics For neurosurgery: modeling issues.

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation

A framework for predictive modeling of anatomical deformations

Finite element modeling of brain tumor mass-effect from 3d medical images