Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei

[1]  D. Koundal,et al.  Deep Neural Networks for Medical Image Segmentation , 2022, Journal of healthcare engineering.

[2]  Jinshan Tang,et al.  MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information , 2021, Comput. Biol. Medicine.

[3]  Chuanwang Zhang,et al.  Multi-layer segmentation framework for cell nuclei using improved GVF Snake model, Watershed, and ellipse fitting , 2021, Biomed. Signal Process. Control..

[4]  Bahram Parvin,et al.  An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models , 2021, Bioinform..

[5]  Tetsuya J. Kobayashi,et al.  3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis , 2020, npj Systems Biology and Applications.

[6]  A. M. Hafiz,et al.  A survey on instance segmentation: state of the art , 2020, International Journal of Multimedia Information Retrieval.

[7]  Dimitris N. Metaxas,et al.  Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images , 2020, IEEE Transactions on Medical Imaging.

[8]  Antonella Carbonaro,et al.  Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates , 2020, Computational and structural biotechnology journal.

[9]  Petr Matula,et al.  Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning , 2020, ArXiv.

[10]  Jin Qi,et al.  Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images , 2020, Neurocomputing.

[11]  Dagmar Kainmueller,et al.  An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images , 2020, MIDL.

[12]  Peter Horvath,et al.  3D-Cell-Annotator: an open-source active surface tool for single-cell segmentation in 3D microscopy images , 2020, Bioinformatics.

[13]  Antonio J. Plaza,et al.  Image Segmentation Using Deep Learning: A Survey , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  David Joon Ho,et al.  DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data , 2019, Scientific Reports.

[15]  Thomas Fevens,et al.  Nuclei Segmentation in Histopathological Images Using Two-Stage Learning , 2019, MICCAI.

[16]  Linfeng Yang,et al.  NuSeT: A deep learning tool for reliably separating and analyzing crowded cells , 2019, bioRxiv.

[17]  Eugene W. Myers,et al.  Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[18]  Jozsef Molnar,et al.  3D-Cell-Annotator: an open-source active surface tool for single cell segmentation in 3D microscopy images , 2019, bioRxiv.

[19]  Thomas Walter,et al.  Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map , 2019, IEEE Transactions on Medical Imaging.

[20]  G. D. da Rocha,et al.  Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles , 2019, Scientific Reports.

[21]  Jin Tae Kwak,et al.  Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images , 2018, Medical Image Anal..

[22]  Johannes Stegmaier,et al.  CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[23]  Will A. Marshall,et al.  A deep learning-based algorithm for 2-D cell segmentation in microscopy images , 2018, BMC Bioinformatics.

[24]  Min Xu,et al.  Learn to segment single cells with deep distance estimator and deep cell detector , 2018, Comput. Biol. Medicine.

[25]  Carsten Rother,et al.  Panoptic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  S. Savolainen,et al.  Multicellular dosimetric chain for molecular radiotherapy exemplified with dose simulations on 3D cell spheroids. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[27]  Li-Hsin Han,et al.  Modeling Physiological Events in 2D vs. 3D Cell Culture. , 2017, Physiology.

[28]  Surabhi Bhargava,et al.  A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology , 2017, IEEE Transactions on Medical Imaging.

[29]  Vítor M Gaspar,et al.  3D tumor spheroids: an overview on the tools and techniques used for their analysis. , 2016, Biotechnology advances.

[30]  Zoltan Kato,et al.  Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours , 2016, Scientific Reports.

[31]  Ong Kok Haur,et al.  OpenSegSPIM: a user-friendly segmentation tool for SPIM data , 2016, Bioinform..

[32]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[33]  Juho Kannala,et al.  Joint cell segmentation and tracking using cell proposals , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[34]  Hao Chen,et al.  DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Maël Montévil,et al.  SAMA: A Method for 3D Morphological Analysis , 2016, PloS one.

[36]  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.

[37]  Philipp J. Keller,et al.  Real-Time Three-Dimensional Cell Segmentation in Large-Scale Microscopy Data of Developing Embryos. , 2016, Developmental cell.

[38]  Andreas Bartschat,et al.  XPIWIT - an XML pipeline wrapper for the Insight Toolkit , 2015, Bioinform..

[39]  Alexander Schmitz,et al.  Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition , 2015, BMC Bioinformatics.

[40]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[41]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[42]  Minjung Kang,et al.  Stem Cell Reports , Volume 2 Supplemental Information A Rapid and Efficient 2 D / 3 D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell Image Data , 2014 .

[43]  Charles Marion,et al.  ITK: enabling reproducible research and open science , 2014, Front. Neuroinform..

[44]  G. Glatting,et al.  Treatment planning in molecular radiotherapy. , 2013, Zeitschrift fur medizinische Physik.

[45]  Long Chen,et al.  A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images , 2013, BMC Bioinformatics.

[46]  Thomas Boudier,et al.  TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization , 2013, Bioinform..

[47]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[48]  Erik H. W. Meijering,et al.  Cell Segmentation: 50 Years Down the Road [Life Sciences] , 2012, IEEE Signal Processing Magazine.

[49]  Anne E Carpenter,et al.  Annotated high-throughput microscopy image sets for validation , 2012, Nature Methods.

[50]  Armando Varela-Ramírez,et al.  Differential nuclear staining assay for high-throughput screening to identify cytotoxic compounds. , 2011, Current cellular biochemistry.

[51]  Marilena Loizidou,et al.  3D tumour models: novel in vitro approaches to cancer studies , 2011, Journal of Cell Communication and Signaling.

[52]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

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

[54]  Hanchuan Peng,et al.  V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets , 2010, Nature Biotechnology.

[55]  Y Q Guan,et al.  3D boundary extraction of confocal cellular images using higher order statistics , 2009, Journal of microscopy.

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

[57]  Alexandra Branzan Albu,et al.  A Morphology-Based Approach for Interslice Interpolation of Anatomical Slices From Volumetric Images , 2008, IEEE Transactions on Biomedical Engineering.

[58]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[59]  C Wählby,et al.  Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections , 2004, Journal of microscopy.

[60]  L. Vese,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[61]  Kristel Michielsen,et al.  Morphological image analysis , 2000 .

[62]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[63]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[64]  C. Sonnenschein,et al.  SAMA : A Method for 3 D Morphological Analysis , 2016 .

[65]  E. Meijering Cell Segmentation: 50 Years Down the Road [Life Sciences] , 2012, IEEE Signal Processing Magazine.

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

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