CAS: Cell Annotation Software – Research on Neuronal Tissue Has Never Been so Transparent

CAS (Cell Annotation Software) is a novel tool for analysis of microscopic images and selection of the cell soma or nucleus, depending on the research objectives in medicine, biology, bioinformatics, etc. It replaces time-consuming and tiresome manual analysis of single images not only with automatic methods for object segmentation based on the Statistical Dominance Algorithm, but also semi-automatic tools for object selection within a marked region of interest. For each image, a broad set of object parameters is computed, including shape features and optical and topographic characteristics, thus giving additional insight into data. Our solution for cell detection and analysis has been verified by microscopic data and its application in the annotation of the lateral geniculate nucleus has been examined in a case study.

[1]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[2]  Tony J Collins,et al.  ImageJ for microscopy. , 2007, BioTechniques.

[3]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[4]  A. Piórkowski,et al.  Constructing software for analysis of neuron, glial and endothelial cell numbers and density in histological Nissl-stained rodent brain tissue , 2014 .

[5]  Arkadiusz Gertych,et al.  Machine Learning Can Reliably Distinguish Histological Patterns of Micropapillary and Solid Lung Adenocarcinomas , 2016, ITIB.

[6]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[7]  H. Irshad,et al.  Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential , 2014, IEEE Reviews in Biomedical Engineering.

[8]  Márton Gulyás,et al.  AnimalTracker: An ImageJ-Based Tracking API to Create a Customized Behaviour Analyser Program , 2016, Neuroinformatics.

[9]  Arkadiusz Gertych,et al.  Rapid 3-D delineation of cell nuclei for high-content screening platforms , 2016, Comput. Biol. Medicine.

[10]  D. B. Bowling,et al.  The distribution of on‐ and off‐centre X‐ and Y‐like cells in the A layers of the cat's lateral geniculate nucleus. , 1986, The Journal of physiology.

[11]  Arkadiusz Gertych,et al.  Automated Detection of Dual p16/Ki67 Nuclear Immunoreactivity in Liquid-Based Pap Tests for Improved Cervical Cancer Risk Stratification , 2012, Annals of Biomedical Engineering.

[12]  D Ferster,et al.  Relay cell classes in the lateral geniculate nucleus of the cat and the effects of visual deprivation , 1977, The Journal of comparative neurology.

[13]  Karolina Nurzynska,et al.  Shape parameters for automatic classification of snow particles into snowflake and graupel , 2013 .

[14]  Shawn Mikula,et al.  Internet-enabled high-resolution brain mapping and virtual microscopy , 2007, NeuroImage.

[15]  J. Kaas.  Evolution of columns, modules, and domains in the neocortex of primates , 2012, Proceedings of the National Academy of Sciences.

[16]  Karolina Nurzynska,et al.  THE CORRELATION ANALYSIS OF THE SHAPE PARAMETERS FOR ENDOTHELIAL IMAGE CHARACTERISATION , 2016 .

[17]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[18]  Javier DeFelipe,et al.  3D segmentations of neuronal nuclei from confocal microscope image stacks , 2013, Front. Neuroanat..

[19]  W. Singer,et al.  Light and electron microscopic immunocytochemical localization of parvalbumin in the dorsal lateral geniculate nucleus of the cat: Evidence for coexistence with GABA , 1988, The Journal of comparative neurology.

[20]  Adam Piórkowski,et al.  A Statistical Dominance Algorithm for Edge Detection and Segmentation of Medical Images , 2016, ITIB.

[21]  Karolina Nurzynska,et al.  Influence of applied corneal endothelium image segmentation techniques on the clinical parameters , 2017, Comput. Medical Imaging Graph..

[22]  W Singer,et al.  Laminar segregation of afferents to lateral geniculate nucleus of the cat: an analysis of current source density. , 1977, Journal of neurophysiology.

[23]  Przemyslaw Mazurek,et al.  From the slit-island method to the Ising model: Analysis of irregular grayscale objects , 2014, Int. J. Appl. Math. Comput. Sci..

[24]  Jagath C. Rajapakse,et al.  Segmentation of Clustered Nuclei With Shape Markers and Marking Function , 2009, IEEE Transactions on Biomedical Engineering.

[25]  Y. Gerasimenko,et al.  Distribution of 28 kDa Calbindin-Immunopositive Neurons in the Cat Spinal Cord , 2016, Front. Neuroanat..

[26]  L. Chalupa,et al.  The new visual neurosciences , 2014 .

[27]  Stanley R. Sternberg,et al.  Biomedical Image Processing , 1983, Computer.

[28]  Sooyoung Chung,et al.  Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex , 2005, Nature.

[29]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[30]  Bernd Jähne,et al.  Digital Image Processing: Concepts, Algorithms, and Scientific Applications , 1991 .

[31]  M. Pool,et al.  NeuriteTracer: A novel ImageJ plugin for automated quantification of neurite outgrowth , 2008, Journal of Neuroscience Methods.

[32]  Marek Kowal,et al.  Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images , 2013, Comput. Biol. Medicine.

[33]  Kalina Burnat,et al.  Global motion detection is impaired in cats deprived early of pattern vision , 2002, Behavioural Brain Research.

[34]  Sean M. Hartig,et al.  Basic Image Analysis and Manipulation in ImageJ , 2013, Current protocols in molecular biology.

[35]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[36]  M. Bickford,et al.  Neurofilament Proteins in Y-Cells of the Cat Lateral Geniculate Nucleus: Normal Expression and Alteration with Visual Deprivation , 1998, The Journal of Neuroscience.

[37]  Marek Kowal,et al.  Nuclei Recognition Using Iterated Conditional Modes Approach , 2017, CORES.

[38]  Chanho Jung,et al.  Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization , 2010, IEEE Transactions on Biomedical Engineering.

[39]  A. Peters,et al.  The Concept of Cat Primary Visual Cortex , 2002 .

[40]  Alicia Hidalgo,et al.  DeadEasy neurons: Automatic counting of HB9 neuronal nuclei in Drosophila , 2010, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[41]  M. Bickford,et al.  A novel means of Y cell identification in the developing lateral geniculate nucleus of the cat , 2000, Neuroscience Letters.

[42]  Daniel R. Berger,et al.  The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus , 2016, Cell.

[43]  Can Fahrettin Koyuncu,et al.  Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation , 2016, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[44]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[45]  Peter H. Schiller,et al.  Parallel information processing channels created in the retina , 2010, Proceedings of the National Academy of Sciences.

[46]  Jane W Chan,et al.  The Cat Primary Visual Cortex , 2006 .

[47]  K. Sanderson,et al.  The projection of the visual field to the lateral geniculate and medial interlaminar nuclei in the cat , 1971, The Journal of comparative neurology.

[48]  John C. Russ,et al.  The Image Processing Handbook , 2016, Microscopy and Microanalysis.

[49]  Tao Yang,et al.  Robust Neuron Counting Based on Fusion of Shape Map and Multi-cue Learning , 2016, BIH.

[50]  J. Stone,et al.  Relay of receptive-field properties in dorsal lateral geniculate nucleus of the cat. , 1972, Journal of neurophysiology.

[51]  R. J. Mullen,et al.  NeuN, a neuronal specific nuclear protein in vertebrates. , 1992, Development.

[52]  Olli Yli-Harja,et al.  Software for quantification of labeled bacteria from digital microscope images by automated image analysis. , 2005, BioTechniques.

[53]  Anne E Carpenter,et al.  Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software , 2011, Bioinform..

[54]  Francesca Papadopulos,et al.  Common Tasks in Microscopic and Ultrastructural Image Analysis Using ImageJ , 2007, Ultrastructural pathology.

[55]  M. Cynader,et al.  An interdigitated columnar mosaic of cytochrome oxidase, zinc, and neurotransmitter-related molecules in cat and monkey visual cortex. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[56]  Henry Markram,et al.  Computing the size and number of neuronal clusters in local circuits , 2013, Front. Neuroanat..

[57]  Adam Piórkowski,et al.  A review of the efficient algorithm implementation for image processing in the ImageJ and Matlab environments , 2017 .