Machine Learning for Instance Segmentation

Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level. A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms. In this thesis, we develop new algorithms for this task. In the first part we provide, for the first time in this field, a method for training a neural network to predict optimal input data for a watershed algorithm. We demonstrate its superior performance compared to other segmentation methods of its category. In the second part, we develop an efficient watershed-based algorithm for weighted graph partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time. We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge. Our algorithm is currently used by the leaders of two connectomics challenges. Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision

04131 Abstracts Collection -- Geometric Properties from Incomplete Data

From 21.03.04 to 26.03.04, the Dagstuhl Seminar 04131 ``Geometric Properties from Incomplete Data\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

Haptically assisted connection procedure for the reconstruction of dendritic spines

Dendritic spines are thin protrusions that cover the dendritic surface of numerous neurons in the brain and whose function seems to play a key role in neural circuits. The correct segmentation of those structures is difficult due to their small size and the resulting spines can appear incomplete. This paper presents a four-step procedure for the complete reconstruction of dendritic spines. The haptically driven procedure is intended to work as an image processing stage before the automatic segmentation step giving the final representation of the dendritic spines. The procedure is designed to allow both the navigation and the volume image editing to be carried out using a haptic device. A use case employing our procedure together with a commercial software package for the segmentation stage is illustrated. Finally, the haptic editing is evaluated in two experiments; the first experiment concerns the benefits of the force feedback and the second checks the suitability of the use of a haptic device as input. In both cases, the results shows that the procedure improves the editing accuracy

Tomografia estendida : do básico até o mapeamento de cérebro de camundongos

Orientador: Mateus Borba CardosoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb WataghinResumo: Esta tese apresentará uma introdução a imagens de raios-x e como adquirir e processar imagens usando linhas de luz síncrotron. Apresentará os desafios matemáticos e técnicos para reconstruir amostras em três dimensões usando a reconstrução de Tomografia Computadorizada, uma técnica conhecida como CT. Esta técnica tem seu campo de visão limitado ao tamanho da câmera e ao tamanho da iluminação. Uma técnica para ampliar esse campo de visão vai ser apresentada e os desafios técnicos envolvidos para que isso aconteça. Um \textit{pipeline} é proposto e todos os algoritmos necessários foram empacotados em um pacote python chamado Tomosaic. A abordagem baseia-se em adquirir tomogramas parciais em posiçoes pré definidas e depois mesclar os dados em um novo conjunto de dados. Duas maneiras possíveis são apresentadas para essa mescla, uma no domínio das projeções e uma no domínio dos sinogramas. Experimentos iniciais serão então usadas para mostrar que o método proposto funciona com computadores normais. A técnica será aplicada mais tarde para pesquisar a anatomia de cérebros de camundongo completos. Um estudo será apresentado de como obter informação em diferentes escalas do cérebro completo do rato utilizando raios-xAbstract: This thesis will present an introduction to x-ray images and how to acquire and thread images using synchrotron beamlines. It will present the mathematical and technical challenges to reconstruct samples in three dimensions using Computed Tomography reconstruction, a technique known as CT. This technique has a field of view bounded to the camera size and the illumination size. A technique to extended this field of view is going to be presented and the technical challenges involved in order for that to happen will be described. A pipeline is proposed and all the necessary algorithms are contained into a python packaged called Tomosaic. The approach relies on acquired partial tomogram data in a defined grid and later merging the data into a new dataset. Two possible ways are presented in order to that: in the projection domain, and in the sinogram domain. Initial experiments will then be used to show that the pipeline works with normal computers. The technique will be later applied to survey the whole anatomy of whole mouse brains. A study will be shown of how to get the complete range of scales of the mouse brain using x-ray tomography at different resolutionsDoutoradoFísicaDoutor em Ciências163304/2013-01247445/2013, 1456912/2014CNPQCAPE