Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

New-onset epilepsy: considerations for initial and follow-up MRI to detect brain tumor

Seizures are often the presenting symptoms of a cerebral tumor and may precede its diagnosis by many years. The article under evaluation searched two large English registries for patients admitted for new-onset epilepsy. The risk of subsequently being diagnosed with a malignant brain tumor was found to be 26-fold higher compared with controls, persisted over many years and was accentuated in young patients. Recently, surgical advances have led to a significant decrease in surgical morbidities, making surgery the first treatment option for gliomas, especially low-grade gliomas. This paradigm shift warrants a consequent diagnostic workup (MRI) in patients at risk for low-grade glioma - that is, patients with new-onset epilepsy. The study is discussed in the context of the ongoing debate on neuroimaging after new-onset epilepsy.

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

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.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Characterization of the 9L gliosarcoma implanted in the Fischer rat: an orthotopic model for a grade IV brain tumor.

Among rodent models for brain tumors, the 9L gliosarcoma is one of the most widely used. Our 9L-European Synchrotron Radiation Facility (ESRF) model was developed from cells acquired at the Brookhaven National Laboratory (NY, USA) in 1997 and implanted in the right caudate nucleus of syngeneic Fisher rats. It has been largely used by the user community of the ESRF during the last decade, for imaging, radiotherapy, and chemotherapy, including innovative treatments based on particular irradiation techniques and/or use of new drugs. This work presents a detailed study of its characteristics, assessed by magnetic resonance imaging (MRI), histology, immunohistochemistry, and cytogenetic analysis. The data used for this work were from rats sampled in six experiments carried out over a 3-year period in our lab (total number of rats = 142). The 9L-ESRF tumors were induced by a stereotactic inoculation of 10(4) 9L cells in the right caudate nucleus of the brain. The assessment of vascular parameters was performed by MRI (blood volume fraction and vascular size index) and by immunostaining of vessels (rat endothelial cell antigen-1 and type IV collagen). Immunohistochemistry and regular histology were used to describe features such as tumor cell infiltration, necrosis area, nuclear pleomorphism, cellularity, mitotic characteristics, leukocytic infiltration, proliferation, and inflammation. Moreover, for each of the six experiments, the survival of the animals was assessed and related to the tumor growth observed by MRI or histology. Additionally, the cytogenetic status of the 9L cells used at ESRF lab was investigated by comparative genomics hybridization analysis. Finally, the response of the 9L-ESRF tumor to radiotherapy was estimated by plotting the survival curves after irradiation. The median survival time of 9L-ESRF tumor-bearing rats was highly reproducible (19-20 days). The 9L-ESRF tumors presented a quasi-exponential growth, were highly vascularized with a high cellular density and a high proliferative index, accompanied by signs of inflammatory responses. We also report an infiltrative pattern which is poorly observed on conventional 9 L tumor. The 9L-ESRF cells presented some cytogenetic specificities such as altered regions including CDK4, CDKN2A, CDKN2B, and MDM2 genes. Finally, the lifespan of 9L-ESRF tumor-bearing rats was enhanced up to 28, 35, and 45 days for single doses of 10, 20, and 2 × 20 Gy, respectively. First, this report describes an animal model that is used worldwide. Second, we describe few features typical of our model if compared to other 9L models worldwide. Altogether, the 9L-ESRF tumor model presents characteristics close to the human high-grade gliomas such as high proliferative capability, high vascularization and a high infiltrative pattern. Its response to radiotherapy demonstrates its potential as a tool for innovative radiotherapy protocols.

Atopic conditions and brain tumor risk in children and adolescents--an international case-control study (CEFALO)

BACKGROUND A number of epidemiological studies indicate an inverse association between atopy and brain tumors in adults, particularly gliomas. We investigated the association between atopic disorders and intracranial brain tumors in children and adolescents, using international collaborative CEFALO data. PATIENTS AND METHODS CEFALO is a population-based case-control study conducted in Denmark, Norway, Sweden, and Switzerland, including all children and adolescents in the age range 7-19 years diagnosed with a primary brain tumor between 2004 and 2008. Two controls per case were randomly selected from population registers matched on age, sex, and geographic region. Information about atopic conditions and potential confounders was collected through personal interviews. RESULTS In total, 352 cases (83%) and 646 controls (71%) participated in the study. For all brain tumors combined, there was no association between ever having had an atopic disorder and brain tumor risk [odds ratio 1.03; 95% confidence interval (CI) 0.70-1.34]. The OR was 0.76 (95% CI 0.53-1.11) for a current atopic condition (in the year before diagnosis) and 1.22 (95% CI 0.86-1.74) for an atopic condition in the past. Similar results were observed for glioma. CONCLUSIONS There was no association between atopic conditions and risk of all brain tumors combined or of glioma in particular. Stratification on current or past atopic conditions suggested the possibility of reverse causality, but may also the result of random variation because of small numbers in subgroups. In addition, an ongoing tumor treatment may affect the manifestation of atopic conditions, which could possibly affect recall when reporting about a history of atopic diseases. Only a few studies on atopic conditions and pediatric brain tumors are currently available, and the evidence is conflicting.

A Hybrid Model for Multimodal Brain Tumor Segmentation

We present a fully automatic segmentation method for multi-modal brain tumor segmentation. The proposed generative-discriminative hybrid model generates initial tissue probabilities, which are used subsequently for enhancing the classi�cation and spatial regularization. The model has been evaluated on the BRATS2013 training set, which includes multimodal MRI images from patients with high- and low-grade gliomas. Our method is capable of segmenting the image into healthy (GM, WM, CSF) and pathological tissue (necrotic, enhancing and non-enhancing tumor, edema). We achieved state-of-the-art performance (Dice mean values of 0.69 and 0.8 for tumor subcompartments and complete tumor respectively) within a reasonable timeframe (4 to 15 minutes).

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.

CCDC26, CDKN2BAS, RTEL1 and TERT Polymorphisms in pediatric brain tumor susceptibility.

The role of genetic polymorphisms in pediatric brain tumor (PBT) etiology is poorly understood. We hypothesized that single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) on adult glioma would also be associated with PBT risk. The study is based on the Cefalo study, a population-based multicenter case-control study. Saliva DNA from 245 cases and 489 controls, aged 7-19 years at diagnosis/reference date, was extracted and genotyped for 29 SNPs reported by GWAS to be significantly associated with risk of adult glioma. Data were analyzed using unconditional logistic regression. Stratified analyses were performed for two histological subtypes: astrocytoma alone and the other tumor types combined. The results indicated that four SNPs, CDKN2BAS rs4977756 (p = 0.036), rs1412829 (p = 0.037), rs2157719 (p = 0.018) and rs1063192 (p = 0.021), were associated with an increased susceptibility to PBTs, whereas the TERT rs2736100 was associated with a decreased risk (p = 0.018). Moreover, the stratified analyses showed a decreased risk of astrocytoma associated with RTEL1 rs6089953, rs6010620 and rs2297440 (p trend = 0.022, p trend = 0.042, p trend = 0.029, respectively) as well as an increased risk of this subtype associated with RTEL1 rs4809324 (p trend = 0.033). In addition, SNPs rs10464870 and rs891835 in CCDC26 were associated with an increased risk of non-astrocytoma tumor subtypes (p trend = 0.009, p trend = 0.007, respectively). Our findings indicate that SNPs in CDKN2BAS, TERT, RTEL1 and CCDC26 may be associated with the risk of PBTs. Therefore, we suggest that pediatric and adult brain tumors might share common genetic risk factors and similar etiological pathways.