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.

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.

Intestinal glucose absorption but not endogenous glucose production differs between colostrum- and formula-fed neonatal calves

Glucose supply markedly changes during the transition to extrauterine life. In this study, we investigated diet effects on glucose metabolism in neonatal calves. Calves were fed colostrum (C; n = 7) or milk-based formula (F; n = 7) with similar nutrient content up to d 4 of life. Blood plasma samples were taken daily before feeding and 2 h after feeding on d 4 to measure glucose, lactate, nonesterified fatty acids, protein, urea, insulin, glucagon, and cortisol concentrations. On d 2, additional blood samples were taken to measure glucose first-pass uptake (FPU) and turnover by oral [U-(13)C]-glucose and i.v. [6,6-(2)H(2)]-glucose infusion. On d 3, endogenous glucose production and gluconeogenesis were determined by i.v. [U-(13)C]-glucose and oral deuterated water administration after overnight feed deprivation. Liver tissue was obtained 2 h after feeding on d 4 and glycogen concentration and activities and mRNA abundance of gluconeogenic enzymes were measured. Plasma glucose and protein concentrations and hepatic glycogen concentration were higher (P < 0.05), whereas plasma urea, glucagon, and cortisol (d 2) concentrations as well as hepatic pyruvate carboxylase mRNA level and activity were lower (P < 0.05) in group C than in group F. Orally administered [U-(13)C]-glucose in blood was higher (P < 0.05) but FPU tended to be lower (P < 0.1) in group C than in group F. The improved glucose status in group C resulted from enhanced oral glucose absorption. Metabolic and endocrine changes pointed to elevated amino acid degradation in group F, presumably to provide substrates to meet energy requirements and to compensate for impaired oral glucose uptake.

Brain areas involved in medial temporal lobe seizures: a principal component analysis of ictal SPECT data

The study describes brain areas involved in medial temporal lobe (mTL) seizures of 12 patients. All patients showed so-called oro-alimentary behavior within the first 20 s of clinical seizure manifestation characteristic of mTL seizures. Single photon emission computed tomography (SPECT) images of regional cerebral blood flow (rCBF) were acquired from the patients in ictal and interictal phases and from normal volunteers. Image analysis employed categorical comparisons with statistical parametric mapping and principal component analysis (PCA) to assess functional connectivity. PCA supplemented the findings of the categorical analysis by decomposing the covariance matrix containing images of patients and healthy subjects into distinct component images of independent variance, including areas not identified by the categorical analysis. Two principal components (PCs) discriminated the subject groups: patients with right or left mTL seizures and normal volunteers, indicating distinct neuronal networks implicated by the seizure. Both PCs were correlated with seizure duration, one positively and the other negatively, confirming their physiological significance. The independence of the two PCs yielded a clear clustering of subject groups. The local pattern within the temporal lobe describes critical relay nodes which are the counterpart of oro-alimentary behavior: (1) right mesial temporal zone and ipsilateral anterior insula in right mTL seizures, and (2) temporal poles on both sides that are densely interconnected by the anterior commissure. Regions remote from the temporal lobe may be related to seizure propagation and include positively and negatively loaded areas. These patterns, the covarying areas of the temporal pole and occipito-basal visual association cortices, for example, are related to known anatomic paths.

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.