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Renal Medullary and Cortical Correlates in Fibrosis, Epithelial Mass, Microvascularity, and Microanatomy Using Whole Slide Image Analysis Morphometry

• Authors : Farris, Alton B.; Ellis, Carla L.; Rogers, Thomas E.

Subjects: Biology and Life Sciences; Developmental Biology; Fibrosis

• Source: Farris, Alton B., Carla L. Ellis, Thomas E. Rogers, Diane Lawson, Cynthia Cohen, and Seymour Rosen. 2016. “Renal Medullary and Cortical Correlates in Fibrosis, Epithelial Mass, Microvascularity, and

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DCMQI: An open source library for standardized communication of quantitative image analysis results using DICOM

• Authors : Herz, Christian; Fillion-Robin, Jean-Christophe; Onken, Michael

• Source: Quick submit: 2017-08-08T09:33:15-0400Herz, Christian, Jean-Christophe Fillion-Robin, Michael Onken, Jörg Riesmeier, Andras Lasso, Csaba Pinter, Gabor Fichtinger, et al. Forthcoming. DCMQI: An

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Comprehensive high-throughput image analysis for therapeutic efficacy of architecturally complex heterotypic organoids

• Authors : Bulin, Anne-Laure; Broekgaarden, Mans; Hasan, Tayyaba

• Source: Bulin, Anne-Laure, Mans Broekgaarden, and Tayyaba Hasan. 2017. “Comprehensive high-throughput image analysis for therapeutic efficacy of

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Learning from Cell-to-Cell Heterogeneity through Computer Vision: Fully Automated Image Analysis Identifies Relationships among Growth Cone Morphology, Rho GTPase Activation Motifs, and Neurite Elongation

• Authors : Bagonis, Maria M.;

Subjects: growth cone; computer vision

• Source: Bagonis, Maria M. 2017. Learning from Cell-to-Cell Heterogeneity through Computer Vision: Fully Automated Image Analysis Identifies

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A guide to human in vivo microcirculatory flow image analysis

• Authors : Massey, Michael J.; Shapiro, Nathan

• Source: Massey, Michael J., and Nathan I. Shapiro. 2016. “A guide to human in vivo microcirculatory flow image analysis.” Critical Care 20

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High Content Image Analysis for Human H4 Neuroglioma Cells Exposed to CuO Nanoparticles

• Authors : Li, Fuhai; Zhou, Xiaobo; Zhu, Jinmin

• Source: Li, Fuhai, Xiaobo Zhou, Jinmin Zhu, Jinwen Ma, Xudong Huang, and Stephen TC Wong. 2007. High content image analysis for human H4

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Deformable organisms for automatic medical image analysis

• Authors : McInerney, Tim; Hamarneh, Ghassan; Shenton, Martha

Subjects: medical image segmentation; deformable models; intelligent agents

• Source: McInerney, Tim, Ghassan Hamarneh, Martha Shenton, and Demetri Terzopoulos. 2002. “Deformable Organisms for Automatic Medical Image

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High Content Image Analysis Identifies Novel Regulators of Synaptogenesis in a High-Throughput RNAi Screen of Primary Neurons

• Authors : Nieland, Thomas; Saulnier, Jessica; Johnson, Caroline

Subjects: Biology and Life Sciences; Anatomy; Nervous System

• Source: Nieland, Thomas J. F., David J. Logan, Jessica Saulnier, Daniel Lam, Caroline Johnson, David E. Root, Anne E. Carpenter, and Bernardo L. Sabatini. 2014. “High Content

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Tumor Profiling from Pathology Images using Deep Learning

• Authors : Bango, Clyde;

Subjects: Computer Aided Diagnosis; Convolutional Neural Networks (CNN); Deep Learning

• Source: Bango, Clyde. 2021. Tumor Profiling from Pathology Images using Deep Learning. Master's thesis, Harvard University Division of Continuing Education.

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Quantitative analysis of dynamic tumor cell phenotypes regulated by tumor associated macrophages.

• Authors : Luthria, Gaurav; Miller, Miles

Subjects: Cancer Biology; Computational biology; Image analysis

• Source: Luthria, Gaurav. 2021. Quantitative analysis of dynamic tumor cell phenotypes regulated by tumor associated macrophages.. Doctoral dissertation, Harvard University

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