The PASCAL Visual Object Classes Challenge

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal–regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

/pdf/whole-brain-activity-mapping-onto-a-zebrafish-brain-atlas-nee8s22wtm.pdf

Whole-brain activity mapping onto a zebrafish brain atlas

http://library.um.ac.id/free-contents/downloadpdf.php/buku/multivariate-observations-g-a-f-seber-11683.pdf

Multivariate observations / G.A.F. Seber

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

/pdf/tissue-clearing-and-its-applications-in-neuroscience-22y6q3a8eh.pdf

Tissue clearing and its applications in neuroscience

https://dash.harvard.edu/bitstream/handle/1/22543065/nihms577899.pdf?sequence=3

Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior.

Precise three-dimensional (3D) mapping of a large number of gene expression patterns, neuronal types and connections to an anatomical reference helps us to understand the vertebrate brain and its development We developed the Virtual Brain Explorer (ViBE-Z), a software that automatically maps gene expression data with cellular resolution to a 3D standard larval zebrafish (Danio rerio) brain ViBE-Z enhances the data quality through fusion and attenuation correction of multiple confocal microscope stacks per specimen and uses a fluorescent stain of cell nuclei for image registration It automatically detects 14 predefined anatomical landmarks for aligning new data with the reference brain ViBE-Z performs colocalization analysis in expression databases for anatomical domains or subdomains defined by any specific pattern; here we demonstrate its utility for mapping neurons of the dopaminergic system The ViBE-Z database, atlas and software are provided via a web interface

ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains

We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.

Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation

We propose a clustering method that considers non-rigid alignment of samples. The motivation for such a clustering is training of object detectors that consist of multiple mixture components. In particular, we consider the deformable part model (DPM) of Felzenszwalb et al., where each mixture component includes a learned deformation model. We show that alignment based clustering distributes the data better to the mixture components of the DPM than previous methods. Moreover, the alignment helps the non-convex optimization of the DPM find a consistent placement of its parts and, thus, learn more accurate part filters.

/pdf/training-deformable-object-models-for-human-detection-based-4d702odmx3.pdf

Training Deformable Object Models for Human Detection Based on Alignment and Clustering

One fundamental step in many state of the art optical flow methods is the initial estimation of reliable correspondences. It is well-established to extract and match features such as HOG to handle large displacements. We propose a combinatorial refinement of the initial matching. Optimization is done in the space of affine motion, where we regularize between neighboring points and similar regions. The evaluation on the MPI-Sintel dataset shows that the proposed method removes outliers from the initial matching and increases the number of reliable matches. The proposed refinement improves all optical flow algorithms that build upon pre-computed correspondences.

/pdf/combinatorial-regularization-of-descriptor-matching-for-5eir3t83st.pdf

Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation

Comparison of different object instances is hard due to the large intra-class variability. Part of this variability is due to viewpoint and pose, another due to subcategories and texture. The variability due to mild viewpoint changes, can be normalized out by aligning the samples. In contrast to the classical Procrustes distance, we propose distances based on non-rigid alignment and show that this increases performance in nearest neighbor tasks. We also investigate which matching costs and which optimization techniques are most appropriate in this context.

/pdf/distances-based-on-non-rigid-alignment-for-comparison-of-v56ny6mi69.pdf

Benjamin Drayer

Papers

ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains

Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation

Training Deformable Object Models for Human Detection Based on Alignment and Clustering

Combinatorial Regularization of Descriptor Matching for Optical Flow Estimation

Distances Based on Non-rigid Alignment for Comparison of Different Object Instances