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.

Classification of organisms previously reported as the SP and Stewart-Letscher groups, with descriptions of Necropsobacter gen. nov. and of Necropsobacter rosorum sp. nov. for organisms of the SP group

To allow classification of bacteria previously reported as the SP group and the Stewart-Letscher group, 35 isolates from rodents (21), rabbits (eight), a dog and humans (five) were phenotypically and genotypically characterized. Comparison of partial rpoB sequences showed that 34 of the isolates were closely related, demonstrating at least 97.4 % similarity. 16S rRNA gene sequence comparison of 20 selected isolates confirmed the monophyly of the SP group and revealed 98.5 %-100 % similarity between isolates. A blast search using the 16S rRNA gene sequences showed that the highest similarity outside the SP group was 95.5 % to an unclassified rat isolate. The single strain, P625, representing the Stewart-Letscher group showed the highest 16S rRNA gene similarity (94.9-95.5 %) to members of the SP group. recN gene sequence analysis of 11 representative strains resulted in similarities of 97-100 % among the SP group strains, which showed 80 % sequence similarity to the Stewart-Letscher group strain. Sequence similarity values based on the recN gene, indicative for whole genome similarity, showed the SP group being clearly separated from established genera, whereas the Stewart-Letscher group strain was associated with the SP group. A new genus, Necropsobacter gen. nov., with only one species, Necropsobacter rosorum sp. nov., is proposed to include all members of the SP group. The new genus can be separated from existing genera of the family Pasteurellaceae by at least three phenotypic characters. The most characteristic properties of the new genus are that haemolysis is not observed on bovine blood agar, positive reactions are observed in the porphyrin test, acid is produced from (+)-L-arabinose, (+)-D-xylose, dulcitol, (+)-D-galactose, (+)-D-mannose, maltose and melibiose, and negative reactions are observed for symbiotic growth, urease, ornithine decarboxylase and indole. Previous publications have documented that both ubiquinones and demethylmenaquinone were produced by the proposed type strain of the new genus, Michel A/76(T), and that the major polyamine of representative strains (type strain not included) of the genus is 1,3-diaminopropane, spermidine is present in moderate amounts and putrescine and spermine are detectable only in minor amounts. The major fatty acids of strain Michel A/76(T) are C(14 : 0), C(16 : 0), C(16:1)omega7c and summed feature C(14 : 0) 3-OH/iso-C(16 : 1) I. This fatty acid profile is typical for members of the family Pasteurellaceae. The G+C content of DNA of strain Michel A/76(T) was estimated to be 52.5 mol% in a previous investigation. The type strain is P709(T) ( = Michel A/76(T) = CCUG 28028(T) = CIP 110147(T) = CCM 7802(T)).

Proposal of Bisgaardia hudsonensis gen. nov., sp. nov. and an additional genomospecies, isolated from seals, as new members of the family Pasteurellaceae

Phenotypic and phylogenetic studies were performed on eight Gram-negative-staining, rod-shaped bacteria isolated from seals. Biochemical and physiological studies showed identical profiles for all of the isolates and indicated that they were related to the family Pasteurellaceae. 16S rRNA gene sequencing demonstrated that the organism represented a distinct cluster with two sublines within the family Pasteurellaceae with <96% sequence similarity to any recognized species. Multilocus sequence analysis (MLSA) including rpoB, infB and recN genes further confirmed these findings with the eight isolates forming a genus-like cluster with two branches. Genome relatedness as deduced from recN gene sequences suggested that the isolates represented a new genus with two species. On the basis of the results of the phylogenetic analysis and phenotypic criteria, it is proposed that these bacteria from seals are classified as Bisgaardia hudsonensis gen. nov., sp. nov. (the type species) and Bisgaardia genomospecies 1. The G+C content of the DNA was 39.5 mol%. The type strain of Bisgaardia hudsonensis gen. nov., sp. nov. is M327/99/2(T) (=CCUG 43067(T)=NCTC 13475(T)=98-D-690B(T)) and the reference strain of Bisgaardia genomospecies 1 is M1765/96/5 (=CCUG 59551=NCTC 13474).

Basfia succiniciproducens gen. nov., sp. nov., a new member of the family Pasteurellaceae isolated from bovine rumen

Gram-negative, coccoid, non-motile bacteria that are catalase-, urease- and indole-negative, facultatively anaerobic and oxidase-positive were isolated from the bovine rumen using an improved selective medium for members of the Pasteurellaceae. All strains produced significant amounts of succinic acid under anaerobic conditions with glucose as substrate. Phenotypic characterization and multilocus sequence analysis (MLSA) using 16S rRNA, rpoB, infB and recN genes were performed on seven independent isolates. All four genes showed high sequence similarity to their counterparts in the genome sequence of the patent strain MBEL55E, but less than 95 % 16S rRNA gene sequence similarity to any other species of the Pasteurellaceae. Genetically these strains form a very homogeneous group in individual as well as combined phylogenetic trees, clearly separated from other genera of the family from which they can also be separated based on phenotypic markers. Genome relatedness as deduced from the recN gene showed high interspecies similarities, but again low similarity to any of the established genera of the family. No toxicity towards bovine, human or fish cells was observed and no RTX toxin genes were detected in members of the new taxon. Based on phylogenetic clustering in the MLSA analysis, the low genetic similarity to other genera and the phenotypic distinction, we suggest to classify these bovine rumen isolates as Basfia succiniciproducens gen. nov., sp. nov. The type strain is JF4016(T) (=DSM 22022(T) =CCUG 57335(T)).

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).