Phylogenomische Untersuchungen zur Aufklärung des Metazoa-Stammbaums

Zentrales Thema der Arbeit war die Aufklärung von Verwandtschaftsverhältnissen im „Tree of Life“ der vielzelligen Tiere (Metazoa) unter Einsatz großer DNA-Sequenzdatensätze und phylogenomischer Methoden. Zur Untersuchung der internen Phylogenie der Syndermata (= meist freilebende Rädertiere („Rotifera“) + endoparasitische Kratzwürmer (Acanthocephala)) sowie ihrer Position im Metazoen-Stammbaum wurden insgesamt sieben neue mitochondriale (mt) Genome sowie neue Transkriptom-Sequenzdaten von sieben verschiedenen Syndermata-Spezies generiert und/oder analysiert. Die Stammbaumrekonstruktionen auf Grundlage dieser sowie orthologer Sequenzen anderer Spezies in Form von phylogenomischen Datensätzen mit bis zu 82.000 Aminosäurepositionen ergaben folgende Aussagen zur Evolution: (i) Innerhalb der Acanthocephala bilden monophyletische Palaeacanthocephala das Schwestertaxon zu den Eoacanthocephala. Die Archiacanthocephala sind Schwestertaxon zu allen vorgenannten. (ii) Innerhalb der Syndermata bilden die epizoisch lebenden Seisonidea das Schwestertaxon zu den endoparasitischen Acanthocephala (= Pararotatoria), die Bdelloidea sind das Schwestertaxon zu den Pararotatoria (= Hemirotifera) und die Monogononta das Schwestertaxon zu den Hemirotifera. Die klassischen Eurotatoria (= Bdelloidea + Monogononta) sind demnach paraphyletisch. (iii) Innerhalb der Metazoa bilden die Syndermata gemeinsam mit den Gnathostomulida die Gnathifera. Diese sind die Schwestergruppe zu allen anderen Spiralia-Taxa, welche sich in Rouphozoa (= Platyhelminthes + Gastrotricha) sowie die Lophotrochozoa aufspalten. Die Platyzoa (= Gnathifera + Platyhelminthes + Gastrotricha) sind demnach paraphyletisch. Diese phylogenetischen Hypothesen wurden im Hinblick auf ihre Implikationen für die Evolution morphologischer und ökologischer Merkmale interpretiert. Demnach sind während der Evolution dieser Tiergruppen mehrfach sekundäre Verlustereignisse von komplexen morphologischen Merkmalen aufgetreten (laterale sensorische Organe innerhalb der Acanthocephala und das Räderorgan (Corona) innerhalb der Syndermata), was die Verwendung dieser Merkmale im Sinne einer klassisch-morphologischen Phylogenetik kritisch erscheinen lässt. Der Endoparasitismus der Acanthocephala hat sich wahrscheinlich über ein epizoisches Zwischenstadium, wie man es heute noch bei den Seisonidea findet, entwickelt. Der letzte gemeinsame Vorfahre der Spiralia war vermutlich klein und unsegmentiert und besaß keine echte Leibeshöhle (Coelom). Demnach hätten sich Segmentierung und Coelome innerhalb der Metazoa mehrfach unabhängig voneinander (konvergent) entwickelt. Die Arbeit beinhaltete folgende weitere, zum Teil methodische Aspekte: (i) die Analyse der Architektur der mt Genome der Monogononta bestätigte die aberrante Organisation in zwei Subgenomen für die Brachionidae. (ii) Eine Prüfung der Tauglichkeit ribosomaler Proteine für molekular-phylogenetische Arbeiten ergab das Vorhandensein widersprüchlicher phylogenetischer Signale in diesen speziellen Proteinsequenzen. (iii) Es konnte nachgewiesen werden, dass systematische Fehler wie „long-branch attraction“ bei der Positionierung der Syndermata im Stammbaum der Metazoa eine große Rolle spielen und adressiert werden müssen.

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.

Automated cement segmentation in vertebroplasty

Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.

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.

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.

Pathology hinting as the combination of automatic segmentation with a statistical shape model

With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Seeing Spaces: An Eye-Tracking Study Of Speech Segmentation

Speech is often a multimodal process, presented audiovisually through a talking face. One area of speech perception influenced by visual speech is speech segmentation, or the process of breaking a stream of speech into individual words. Mitchel and Weiss (2013) demonstrated that a talking face contains specific cues to word boundaries and that subjects can correctly segment a speech stream when given a silent video of a speaker. The current study expanded upon these results, using an eye tracker to identify highly attended facial features of the audiovisual display used in Mitchel and Weiss (2013). In Experiment 1, subjects were found to spend the most time watching the eyes and mouth, with a trend suggesting that the mouth was viewed more than the eyes. Although subjects displayed significant learning of word boundaries, performance was not correlated with gaze duration on any individual feature, nor was performance correlated with a behavioral measure of autistic-like traits. However, trends suggested that as autistic-like traits increased, gaze duration of the mouth increased and gaze duration of the eyes decreased, similar to significant trends seen in autistic populations (Boratston & Blakemore, 2007). In Experiment 2, the same video was modified so that a black bar covered the eyes or mouth. Both videos elicited learning of word boundaries that was equivalent to that seen in the first experiment. Again, no correlations were found between segmentation performance and SRS scores in either condition. These results, taken with those in Experiment, suggest that neither the eyes nor mouth are critical to speech segmentation and that perhaps more global head movements indicate word boundaries (see Graf, Cosatto, Strom, & Huang, 2002). Future work will elucidate the contribution of individual features relative to global head movements, as well as extend these results to additional types of speech tasks.

Visual Speech Segmentation: Using Facial Cues to Locate Word Boundaries in Continuous Speech

Speech is typically a multimodal phenomenon, yet few studies have focused on the exclusive contributions of visual cues to language acquisition. To address this gap, we investigated whether visual prosodic information can facilitate speech segmentation. Previous research has demonstrated that language learners can use lexical stress and pitch cues to segment speech and that learners can extract this information from talking faces. Thus, we created an artificial speech stream that contained minimal segmentation cues and paired it with two synchronous facial displays in which visual prosody was either informative or uninformative for identifying word boundaries. Across three familiarisation conditions (audio stream alone, facial streams alone, and paired audiovisual), learning occurred only when the facial displays were informative to word boundaries, suggesting that facial cues can help learners solve the early challenges of language acquisition.

Current Changes in the Lifestyle and Mental Health of Teenagers

The group studied 1,253 students from various types of schools chosen randomly from those in Prague and Budejovice in order to evaluate the life styles, prevailing value standards, attitudes and behavioural patterns of Czech adolescents. The respondents (including 614 men and 639 women with an average age of 16.4 years) completed questionnaires containing standard scales focusing on feelings about social life, conservative and authoritarian tendencies, levels of self-esteem, general health, eating attitudes and behaviour The adolescents showed a relatively high level of conformity with authoritarian, conservative tendencies and with a dictate of power, rigid conventionality, ethnocentrism and low inner tolerance of differences, their scores being higher than those found in Western European countries. These tendencies were stronger among students outside Prague and those attending vocational schools. As the level of education rose, the sense of fatality and social determination decreased, indicating a higher share of responsibility for events in the surrounding world. When changes of life style were considered, adolescents can be expected to adapt more easily to more risky, socially attractive and manifest models of attitudes and behaviour. On the one hand, adolescents were often involved in sports, and young women in particular often showed a extreme concern and care for their own bodies. On the other hand, one quarter of respondents smoked, one fifth reported serious problems with alcohol and one quarter had already had some experience with drugs. One third of young men and one quarter of young women reported regular consumption of alcohol, and 6.5 percent of men and 3.6 percent of women regularly smoked marihuana or hashish. For the majority of adolescents, life conditions and conformity seem to be more important than the sense of active choice and responsibility for one's own life.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Early assessment of brain maturation by MR imaging segmentation in neonates and premature infants

PURPOSE: We evaluated the impact of premature extrauterine life on brain maturation. PATIENTS AND METHODS: Twelve neonates underwent MR imaging at 40 (39.64 +/- 0.98) weeks (full term). Fifteen premature infants underwent 2 MR imaging examinations, after birth (preterm at birth) and at 40 weeks (41.03 +/- 1.33) (preterm at term). A 3D MR imaging technique was used to measure brain volumes compared with intracranial volume: total brain volume, cortical gray matter, myelinated white matter, unmyelinated white matter, basal ganglia (BG), and CSF. RESULTS: The average absolute volume of intracranial volume (269.8 mL +/- 36.5), total brain volume (246.5 +/- 32.3), cortical gray matter (85.53 mL +/- 22.23), unmyelinated white matter (142.4 mL +/-14.98), and myelinated white matter (6.099 mL +/-1.82) for preterm at birth was significantly lower compared with that for the preterm at term: the average global volume of intracranial volume (431.7 +/- 69.98), total brain volume (391 +/- 66,1), cortical gray matter (179 mL +/- 41.54), unmyelinated white matter (185.3 mL +/- 30.8), and myelinated white matter (10.66 mL +/- 3.05). It was also lower compared with that of full-term infants: intracranial volume (427.4 mL +/- 53.84), total brain volume (394 +/- 49.22), cortical gray matter (181.4 +/- 29.27), unmyelinated white matter (183.4 +/- 27.37), and myelinated white matter (10.72 +/- 4.63). The relative volume of cortical gray matter (30.62 +/- 5.13) and of unmyelinated white matter (53.15 +/- 4.8) for preterm at birth was significantly different compared with the relative volume of cortical gray matter (41.05 +/- 5.44) and of unmyelinated white matter (43.22 +/- 5.11) for the preterm at term. Premature infants had similar brain tissue volumes at 40 weeks to full-term infants. CONCLUSION: MR segmentation techniques demonstrate that cortical neonatal maturation in moderately premature infants at term and term-born infants was similar.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.