3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging
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A. Corwin | Yingnan Ju | C. Surrette | A. Sood | Sanghee Cho | E. McDonough | F. Ginty | S. Ghose | A. Karunamurthy | L. Falo | Jessica S Martinez | Chrystal Chadwick | Katy Börner | Erich Williams | Jonhan Ho | Yousef Al-Kofahi | R. Rose
[1] Cody N. Heiser,et al. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer , 2023, Cell.
[2] A. Roebroeck,et al. Efficient 3D light-sheet imaging of very large-scale optically cleared human brain and prostate tissue samples , 2022, bioRxiv.
[3] L. Wood,et al. CODA: quantitative 3D reconstruction of large tissues at cellular resolution , 2022, Nature Methods.
[4] Wendy J. Fantl,et al. CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images , 2022, BMC Bioinformatics.
[5] M. Nykter,et al. Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment , 2022, Heliyon.
[6] S. Tavaré,et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment , 2021, Nature Cancer.
[7] Serestina Viriri,et al. Dental Images' Segmentation Using Threshold Connected Component Analysis , 2021, Comput. Intell. Neurosci..
[8] Ellen M. Quardokus,et al. Anatomical structures, cell types and biomarkers of the Human Reference Atlas , 2021, Nature Cell Biology.
[9] P. Hof,et al. 3D molecular phenotyping of cleared human brain tissues with light-sheet fluorescence microscopy , 2021, Communications Biology.
[10] Tyler T. Risom,et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning , 2021, Nature Biotechnology.
[11] J. I. Izpisúa Belmonte,et al. A Single-Cell Transcriptomic Atlas of Human Skin Aging. , 2020, Developmental cell.
[12] David J. Barry,et al. GIANI – open-source software for automated analysis of 3D microscopy images , 2020, bioRxiv.
[13] Daniel J Verdon,et al. Cellular and Molecular Mechanisms of CD8+ T Cell Differentiation, Dysfunction and Exhaustion , 2020, International journal of molecular sciences.
[14] M. Mann,et al. Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin , 2020, Nature Communications.
[15] Adam J. Rubin,et al. Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma , 2020, Cell.
[16] K. Rippe,et al. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming , 2020, Communications Biology.
[17] Matteo Mancini,et al. A multimodal computational pipeline for 3D histology of the human brain , 2020, Scientific Reports.
[18] E. S. Chambers,et al. Skin barrier immunity and ageing , 2019, Immunology.
[19] Katy Borner,et al. Considerations for Using the Vasculature as a Coordinate System to Map All the Cells in the Human Body , 2019, Frontiers in Cardiovascular Medicine.
[20] 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).
[21] Hidekata Hontani,et al. Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation , 2019, International Journal of Computer Assisted Radiology and Surgery.
[22] Sharla L. White,et al. Three-dimensional imaging and quantitative analysis in CLARITY processed breast cancer tissues , 2019, Scientific Reports.
[23] Shila Ghazanfar,et al. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program , 2019, Nature.
[24] Ayush Goyal,et al. Image-based clustering and connected component labeling for rapid automated left and right ventricular endocardial volume extraction and segmentation in full cardiac cycle multi-frame MRI images of cardiac patients , 2019, Medical & Biological Engineering & Computing.
[25] Sébastien Ourselin,et al. A Survey of Methods for 3D Histology Reconstruction , 2018, Medical Image Anal..
[26] B. Chaput,et al. 3D imaging of cleared human skin biopsies using light‐sheet microscopy: A new way to visualize in‐depth skin structure , 2018, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[27] Carolina Wählby,et al. Automated Training of Deep Convolutional Neural Networks for Cell Segmentation , 2017, Scientific Reports.
[28] J. Monserrat,et al. Using surface molecule expression on lymphocytes to classify septic shock patients , 2009, Critical care.
[29] Francesco Saverio Pavone,et al. Clearing of fixed tissue: a review from a microscopist’s perspective , 2016, Journal of biomedical optics.
[30] Jurgen Fripp,et al. MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection. , 2016, Medical physics.
[31] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[32] Shenghao Xu,et al. Supplementary Information , 2014, States at War, Volume 3.
[33] J. D'Orazio,et al. Ultraviolet Radiation, Aging and the Skin: Prevention of Damage by Topical cAMP Manipulation , 2014, Molecules.
[34] N. McGovern,et al. A Three-Dimensional Atlas of Human Dermal Leukocytes, Lymphatics, and Blood Vessels , 2013, The Journal of investigative dermatology.
[35] Qing Li,et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue , 2013, Proceedings of the National Academy of Sciences.
[36] S. Navani,et al. The human protein atlas , 2011 .
[37] Hui Kong,et al. Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting , 2011, IEEE Transactions on Medical Imaging.
[38] R. Clark. Skin-resident T cells: the ups and downs of on site immunity. , 2010, The Journal of investigative dermatology.
[39] P. Jeffrey,et al. Structural Basis of UV DNA-Damage Recognition by the DDB1–DDB2 Complex , 2008, Cell.
[40] A. Cochran,et al. Immunohistochemical characteristics of melanoma , 2008, Journal of cutaneous pathology.
[41] R. Clark,et al. Response to Comment on “The Vast Majority of CLA+ T Cells Are Resident in Normal Skin” , 2006, The Journal of Immunology.
[42] Grégoire Malandain,et al. Fusion of autoradiographs with an MR volume using 2-D and 3-D linear transformations , 2004, NeuroImage.
[43] Daniel Rueckert,et al. Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.
[44] J Streicher,et al. External marker‐based automatic congruencing: A new method of 3D reconstruction from serial sections , 1997, The Anatomical record.
[45] Jianquan Xu,et al. High-Resolution Microscopy for Imaging Cancer Pathobiology , 2019, Current Pathobiology Reports.
[46] G. Fisher,et al. Natural and sun-induced aging of human skin. , 2015, Cold Spring Harbor perspectives in medicine.
[47] Joachim Hornegger,et al. Landmark-constrained 3-D Histological Imaging: A Morphology-preserving Approach , 2011, VMV.
[48] Max A. Viergever,et al. elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.
[49] R. Clark,et al. IL-15 and dermal fibroblasts induce proliferation of natural regulatory T cells isolated from human skin. , 2007, Blood.
[50] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[51] J. Uitto,et al. Expression of p53 in normal sun-exposed and protected skin (type IV-V) in different decades of age. , 2003, Acta dermato-venereologica.
[52] Michael J Ackerman,et al. Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.
[53] Sébastien Ourselin,et al. Reconstructing a 3D structure from serial histological sections , 2001, Image Vis. Comput..
[54] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[55] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[56] T. Sørensen,et al. A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .