larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster
2018-01, Muenzing, Sascha E. A., Strauch, Martin, Truman, James W., Bühler, Katja, Thum, Andreas, Merhof, Dorit
The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0.
Discrimination of cell cycle phases in PCNA-immunolabeled cells
2015, Schönenberger, Felix, Deutzmann, Anja, Ferrando-May, Elisa, Merhof, Dorit
Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images.
We show that the proposed processing chain is capable of automatically classifying cell cycle phases in PCNA-immunolabeled cells from single time point images, independently of the technique of image acquisition. Comparison of confocal and widefield images showed that for the proposed approach, the overall classification accuracy is slightly higher for confocal microscopy images.
Overall, automated identification of cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution, is feasible for both confocal and widefield images.
Automatic framework for tracking honeybee's antennae and mouthparts from low framerate video
2013-09, Shen, Minmin, Szyszka, Paul, Galizia, C. Giovanni, Merhof, Dorit
Automatic tracking of the movement of bee's antennae and mouthparts is necessary for studying associative learning of individuals. However, the problem of tracking them is challenging: First, the different classes of objects possess similar appearance and are close to each other. Second, tracking gaps are often present, due to the low frame-rate of the acquired video and the fast motion of the objects. Most existing insect tracking approaches have been developed for slow moving objects, and are not suitable for this application. In this paper, a novel Bayesian framework is proposed to automatically track bees' antennae and their mouthparts. This framework incorporates information about their kinematics, shape, order and temporal correlation between neighboring frames. Experimental evaluation demonstrates the effectiveness and efficiency of the proposed framework.
Optic radiation fiber tractography in glioma patients based on high angular resolution diffusion imaging with compressed sensing compared with diffusion tensor imaging : initial experience
2013, Kuhnt, Daniela, Bauer, Miriam H. A., Sommer, Jens, Merhof, Dorit, Nimsky, Christopher
Up to now, fiber tractography in the clinical routine is mostly based on diffusion tensor imaging (DTI). However, there are known drawbacks in the resolution of crossing or kissing fibers and in the vicinity of a tumor or edema. These restrictions can be overcome by tractography based on High Angular Resolution Diffusion Imaging (HARDI) which in turn requires larger numbers of gradients resulting in longer acquisition times. Using compressed sensing (CS) techniques, HARDI signals can be obtained by using less non-collinear diffusion gradients, thus enabling the use of HARDI-based fiber tractography in the clinical routine.
Eight patients with gliomas in the temporal lobe, in proximity to the optic radiation (OR), underwent 3T MRI including a diffusion-weighted dataset with 30 gradient directions. Fiber tractography of the OR using a deterministic streamline algorithm based on DTI was compared to tractography based on reconstructed diffusion signals using HARDI+CS.
HARDI+CS based tractography displayed the OR more conclusively compared to the DTI-based results in all eight cases. In particular, the potential of HARDI+CS-based tractography was observed for cases of high grade gliomas with significant peritumoral edema, larger tumor size or closer proximity of tumor and reconstructed fiber tract.
Overcoming the problem of long acquisition times, HARDI+CS seems to be a promising basis for fiber tractography of the OR in regions of disturbed diffusion, areas of high interest in glioma
Interactive tracking of insect posture
2016, Shen, Minmin, Li, Chen, Huang, Wei, Szyszka, Paul, Shirahama, Kimiaki, Grzegorzek, Marcin, Merhof, Dorit, Deussen, Oliver
In this paper, we present an association based tracking approach to track multiple insect body parts in a set of low frame-rate videos. The association is formulated as a MAP problem and solved by the Hungarian algorithm. Different from a traditional track-and-then-rectification scheme, this framework refines the tracking hypotheses in an interactive fashion: it integrates a key frame selection approach to minimize the number of frames for user correction while optimizing the final hypotheses. Given user correction, it takes user inputs to rectify the incorrect hypotheses on the other frames. Thus, the framework improves the tracking accuracy by introducing active key frame selection and interactive components, enabling a flexible strategy to achieve a trade-off between human effort and tracking precision. Given the refined tracks at a bounding box (BB) level, the tip of each body part is estimated, and multiple body parts in a BB are further differentiated. The efficiency and the effectiveness of the framework are verified on challenging video datasets for insect behavioral experiments.
Interactive Framework for Insect Tracking with Active Learning
2014, Shen, Minmin, Huang, Wei, Szyszka, Paul, Galizia, C. Giovanni, Merhof, Dorit
Extracting motion trajectories of insects is an important prerequisite in many behavioral studies. Despite great efforts to design efficient automatic tracking algorithms, tracking errors are unavoidable. In this paper, we propose general principles that help to minimize the human effort required for accurate multi-target tracking in the form of applications that can track the antennae and mouthparts of a honey bee based on a set of low frame rate videos. This interactive framework estimates which key frames will require user correction, i.e. those that are used for user correction, which are used for 1) incrementally learning an object classifier and 2) data association based tracking. To this framework we apply a standard classification algorithm (i.e. naive Bayesian classification) and an association optimization algorithm (i.e. Hungarian algorithm). The precision of tracking results by our framework on real-world video data is above 98%.
Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness
2013-02-20, Richter, Mirco, Merhof, Dorit
In several studies, brain atrophy measured by cortical thickness has shown to be a meaningful biomarker for Alzheimer’s disease. In this research field, the level of granularity at which values are compared is an important aspect. Vertex- and voxel-based approaches can detect atrophy at a very fine scale, but are susceptible to noise from misregistrations and inter-subject differences in the population. Regional approaches are more robust to these kinds of noise, but cannot detect variances at a local scale. In this work, an optimized classifier is presented for a parcellation scheme that provides a trade-off between both paradigms by increasing the granularity of a regional approach. For this purpose, atlas regions are subdivided into gyral and sulcal parts at different height levels. Using two-stage feature selection, optimal gyral and sulcal subregions are determined for the final classification with sparse logistic regression. The robustness was assessed on clinical data by 10- fold cross-validation and by testing the prediction accuracy for unseen individuals. In every aspect, superior classification performance was observed as compared to the original parcellation scheme which can be
explained by the increased locality of cortical thickness measures and
the customized classification approach that reveals interacting regions.
Automated tracking and analysis of behavior in restrained insects
2015, Shen, Minmin, Szyszka, Paul, Deussen, Oliver, Galizia, C. Giovanni, Merhof, Dorit
Insect behavior is often monitored by human observers and measured in the form of binary responses. This procedure is time costly and does not allow a fine graded measurement of behavioral performance in individual animals. To overcome this limitation, we have developed a computer vision system which allows the automated tracking of body parts of restrained insects.
Our system crops a continuous video into separate shots with a static background. It then segments out the insect's head and preprocesses the detected moving objects to exclude detection errors. A Bayesian-based algorithm is proposed to identify the trajectory of each body part.
We demonstrate the application of this novel tracking algorithm by monitoring movements of the mouthparts and antennae of honey bees and ants, and demonstrate its suitability for analyzing the behavioral performance of individual bees using a common associative learning paradigm.
Comparison with existing methods
Our tracking system differs from existing systems in that it does not require each video to be labeled manually and is capable of tracking insects’ body parts even when working with low frame-rate videos. Our system can be generalized for other insect tracking applications.
Our system paves the ground for fully automated monitoring of the behavior of restrained insects and accounts for individual variations in graded behavior.
Evaluation of a human neurite growth assay as specific screen for developmental neurotoxicants
2013-12, Krug, Anne K., Stiegler, Nina, Matt, Florian, Schönenberger, Felix, Merhof, Dorit, Leist, Marcel
Organ-specific in vitro toxicity assays are often highly sensitive, but they lack specificity. We evaluated here examples of assay features that can affect test specificity, and some general procedures are suggested on how positive hits in complex biological assays may be defined. Differentiating human LUHMES cells were used as potential model for developmental neurotoxicity testing. Forty candidate toxicants were screened, and several hits were obtained and confirmed. Although the cells had a definitive neuronal phenotype, the use of a general cell death endpoint in these cultures did not allow specific identification of neurotoxicants. As alternative approach, neurite growth was measured as an organ-specific functional endpoint. We found that neurite extension of developing LUHMES was specifically inhibited by diverse compounds such as colchicine, vincristine, narciclasine, rotenone, cycloheximide, or diquat. These compounds reduced neurite growth at concentrations that did not compromise cell viability, and neurite growth was affected more potently than the integrity of developed neurites of mature neurons. A ratio of the EC50 values of neurite growth inhibition and cell death of >4 provided a robust classifier for compounds associated with a developmental neurotoxic hazard. Screening of unspecific toxicants in the test system always yielded ratios <4. The assay identified also compounds that accelerated neurite growth, such as the rho kinase pathway modifiers blebbistatin or thiazovivin. The negative effects of colchicine or rotenone were completely inhibited by a rho kinase inhibitor. In summary, we suggest that assays using functional endpoints (neurite growth) can specifically identify and characterize (developmental) neurotoxicants.
Automated Image Processing to Quantify Cell Migration
2013-02-20, Shen, Minmin, Zimmer, Bastian, Leist, Marcel, Merhof, Dorit
Methods to evaluate migration capacity of stem cells and the inhibition by chemicals are important for biomedical research. Here, we established an automated image processing framework to quantify migration of human neural crest (NC) cells into an initially empty, circular region of interest (ROI). The ROI is partially filled during the experiment by migrating cells. Based on an image captured only once at the end of the biological experiment, the framework identifies the initial ROI. The identification worked also, when the distribution of surrounding cells showed large heterogeneity. After segmentation, the number of migrated cells was identified. The image processing framework was capable of efficiently quantifying chemical effects on cell migration.