Tracking Neurons In A Free-Moving Caenorhabditis Elegans

Slides:

Advertisements

Similar presentations

How Do You Tell a Blackbird from a Crow? Thomas Berg and Peter N. Belhumeur Columbia University.

Advertisements

A New Algorithm of Fuzzy Clustering for Data with Uncertainties: Fuzzy c-Means for Data with Tolerance Defined as Hyper-rectangles ENDO Yasunori MIYAMOTO.

Learning Techniques for Video Shot Detection Under the guidance of Prof. Sharat Chandran by M. Nithya.

Towards a zoomable cell abstract cell natural coordinate system Data > D Protein Structures from PDB ? A IHGFBCDE > Images from scientific.

Neurocomputing,Neurocomputing, Haojie Li Jinhui Tang Yi Wang Bin Liu School of Software, Dalian University of Technology School of Computer Science,

Artifact and Textured region Detection - Vishal Bangard.

Unsupervised spike sorting with wavelets and super-paramagnetic clustering Rodrigo Quian Quiroga Div. of Biology Caltech.

Exchanging Faces in Images SIGGRAPH ’04 Blanz V., Scherbaum K., Vetter T., Seidel HP. Speaker: Alvin Date: 21 July 2004.

MRI Image Segmentation for Brain Injury Quantification Lindsay Kulkin 1 and Bir Bhanu 2 1 Department of Biomedical Engineering, Syracuse University, Syracuse,

A Study of Approaches for Object Recognition

A Similarity Measure for OWL-S Annotated Web Services Web Intelligence Laboratory, Sharif University of Technology, Tehran, Iran WI 2006 SeyedMohsen (Mohsen)

On the Correlation between Energy and Pitch Accent in Read English Speech Andrew Rosenberg, Julia Hirschberg Columbia University Interspeech /14/06.

Automatic 2D-3D Registration Student: Lingyun Liu Advisor: Prof. Ioannis Stamos.

21 / 06 / 2000Segmentation of Sea-bed Images.1 Josepha UNIA Ecole Centrale de Lyon.

MSU CSE 803 Stockman CV: Matching in 2D Matching 2D images to 2D images; Matching 2D images to 2D maps or 2D models; Matching 2D maps to 2D maps.

Xiaomeng Su & Jon Atle Gulla Dept. of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway June 2004 Semantic.

Preparing for Your Exit Essay By: Adriana Lechuga.

Graph-based consensus clustering for class discovery from gene expression data Zhiwen Yum, Hau-San Wong and Hongqiang Wang Bioinformatics, 2007.

Face Model Fitting with Generic, Group-specific, and Person- specific Objective Functions Chair for Image Understanding and Knowledge-based Systems Institute.

Autonomous Direct 3D Segmentation of Articular Knee Cartilage Author :Enrico Hinrichs, Brian C. Lovell, Ben Appleton, Graham John Galloway Source :Australian.

Optical3D ´05, Gerhard Schall Gerhard Schall, Joseph Newman, Fritz Fraundorfer and Dieter Schmalstieg Construction and Maintenance of Augmented Reality.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A Comparison of SOM Based Document Categorization Systems.

Localization for Mobile Robot Using Monocular Vision Hyunsik Ahn Jan Tongmyong University.

Copyright © 2010 Siemens Medical Solutions USA, Inc. All rights reserved. Hierarchical Segmentation and Identification of Thoracic Vertebra Using Learning-based.

TOPIC : Introduction to Compression Techniques UNIT 5 : BIST and BIST Architectures Module 5.4 Compression Techniques.

Tracking People by Learning Their Appearance Deva Ramanan David A. Forsuth Andrew Zisserman.

Feature based deformable registration of neuroimages using interest point and feature selection Leonid Teverovskiy Center for Automated Learning and Discovery.

Automatic Detection of Social Tag Spams Using a Text Mining Approach Hsin-Chang Yang Associate Professor Department of Information Management National.

General ideas to communicate Show one particular Example of localization based on vertical lines. Camera Projections Example of Jacobian to find solution.

Spatio-Temporal Free-Form Registration of Cardiac MR Image Sequences Antonios Perperidis s /02/2006.

Semantic Wordfication of Document Collections Presenter: Yingyu Wu.

Cooperative Air and Ground Surveillance Wenzhe Li.

Segmentation of 3D Tubular Structures Paul Hernandez-Herrera Computational Biomedicine Lab Advisor: Ioannis A. Kakadiaris and Manos Papadakis 1.

Nicholas lab: C. elegans research Working with worms Postgraduate induction 2013 Yee Lian Chew.

Graph-based Deformable Matching of 3D Line Segments with Application in Protein Fitting 12 1 HANG DOU 1, MATTHEW L BAKER 2, TAO JU Washington University.

Introduction to Related Papers of Vessel Segmentation Methods Advisor : Ku-Yaw Chang Student : Wei-Lu Lin 2015/1/7.

Content-Based MP3 Information Retrieval Chueh-Chih Liu Department of Accounting Information Systems Chihlee Institute of Technology 2005/06/16.

Stable Biometric Features Description (not definition): Biometric features whose value change very infrequently among multiple prints of a finger Deformation.

Multilingual Information Retrieval using GHSOM Hsin-Chang Yang Associate Professor Department of Information Management National University of Kaohsiung.

Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos VC 15/16 – TP10 Advanced Segmentation Miguel Tavares.

A Multilingual Hierarchy Mapping Method Based on GHSOM Hsin-Chang Yang Associate Professor Department of Information Management National University of.

Statistical techniques for video analysis and searching chapter Anton Korotygin.

Multi-Classifier Buried Mine Detection Using MWIR Images Dr. Bo Ling Migma Systems, Inc. Mr. Anh H. Trang Mr. Chung Phan US Army RDECOM April 10, 2007.

An unsupervised conditional random fields approach for clustering gene expression time series Chang-Tsun Li, Yinyin Yuan and Roland Wilson Bioinformatics,

TUMOR BURDEN ANALYSIS ON CT BY AUTOMATED LIVER AND TUMOR SEGMENTATION RAMSHEEJA.RR Roll : No 19 Guide SREERAJ.R ( Head Of Department, CSE)

Automated Geo-referencing of Images Dr. Ronald Briggs Yan Li GeoSpatial Information Sciences The University.

Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection Jiahang Liu, Tao Fang, and Deren Li IEEE TRANSACTIONS ON GEOSCIENCE.

Results from Mean and Variance Calculations The overall mean of the data for all features was for the REF class and for the LE class. The.

Miguel Tavares Coimbra

My Tiny Ping-Pong Helper

Huazhong University of Science and Technology

دانشگاه شهیدرجایی تهران

Computational Neuroanatomy for Dummies

Get the Attention of the Audience

CV: Matching in 2D Matching 2D images to 2D images; Matching 2D images to 2D maps or 2D models; Matching 2D maps to 2D maps MSU CSE 803 Stockman.

تعهدات مشتری در کنوانسیون بیع بین المللی

Department of Psychology University of Washington

Today’s Goals Describe the advantages of C. elegans as a model organism Discuss the life cycle of the nematode Safely and effectively culture a population.

دومین کمیته مترجمین حاکمیت بالینی دانشگاه

Aline Martin ECE738 Project – Spring 2005

Introduction to XYZ using hierarchical models

Introduction to XYZ using hierarchical models

Automated quantitative analysis of locomotory behavior

Bioinformatics, Vol.17 Suppl.1 (ISMB 2001) Weekly Lab. Seminar

Identification of Variation Points Using Dynamic Analysis

Segmentation of Sea-bed Images.

Extracting Why Text Segment from Web Based on Grammar-gram

Introduction to XYZ using hierarchical models

RIO: A Pervasive RFID-based Touch Gesture Interface

Machine Learning.

Presentation transcript:

Tracking Neurons In A Free-Moving Caenorhabditis Elegans Kirillova Irina, 15124

Aim The aim is to study automatic tracking neurons in a moving and deforming brain of C.elegans.

Outline of the presentation Introduction. Automatic tracking neurons in a moving and deforming brain. Results and Conclusion. References.

What is C.elegans? Caenorhabditis elegans is a free-leaving, transparent nematode about 1 mm in length.

Topicality Why is C.elegans interesting for research? Because human body is very complicated, and it's hard to study. At the same time we can consider this worm as a single cell that exhibits a lot of interesting features which are similar to human’s, often named as "fundamental mysteries of modern biology".

Automatic tracking neurons in a moving and deforming brain

Algorithm

Centerline Detection

Straightening and Segmentation

Registration Vector Construction

Clustering Clustering similar registration vectors allows for the identification of that particular neuron across time.

Error Correction This is done by finding the TPS transformation, ut→t⋆: Xt ↦ Xt⋆, that maps the identified points from Xt to the corresponding points in Xt⋆ excluding the point s.

Results and Conclusion This method represents a fully automated algorithm for segmenting and tracking neuron in freely behaving C.elegans. This new approach works better than all previous techniques.

References. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436637/ https://cbs.umn.edu/cgc/what-c-elegans https://en.wikipedia.org/wiki/Caenorhabditis_elegans https://www.labroots.com/trending/neuroscience/7060/tiny-worm-discovery

Thank you for attention!