Hanyi Yu
Emory University
9 Papers
13 Citations
Hanyi Yu is an academic researcher from Emory University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 6 publications.
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Papers
Morphometric Analysis of Retinal Pigment Epithelial Cells From C57BL/6J Mice During Aging.
Yong-Kyu Kim,Yong-Kyu Kim,Hanyi Yu,Vivian Summers,Kevin Donaldson,Salma Ferdous,Debresha Shelton,Nan Zhang,Nan Zhang,Micah A Chrenek,Yi Jiang,Hans E. Grossniklaus,Jeffrey H Boatright,Jun Kong,Jun Kong,John M. Nickerson +15 more
TL;DR: In this article, the authors quantitatively evaluated the changes in orientation and morphometric features of mouse retinal pigment epithelial (RPE) cells in different regions of the eye during aging.
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Clumped Nuclei Segmentation with Adjacent Point Match and Local Shape-Based Intensity Analysis in Fluorescence Microscopy Images
Xiaoyuan Guo,Hanyi Yu,Blair J. Rossetti,George Teodoro,Daniel J. Brat,Jun Kong +5 more
- 01 Jul 2018
TL;DR: Qualitative and quantitative experimental results suggest that the proposed segmentation algorithm is promising for dividing overlapped nuclei in fluorescence microscopy images widely used in various biomedical research.
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Clumped Nuclei Segmentation with Adjacent Point Match and Local Shape based Intensity Analysis for Overlapped Nuclei in Fluorescence In-Situ Hybridization Images.
TL;DR: Qualitative and quantitative experimental results suggest that the proposed segmentation algorithm is promising for dividing overlapped nuclei in fluorescence in situ hybridization microscopy images widely used in various biomedical research.
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Self-supervised semantic segmentation of retinal pigment epithelium cells in flatmount fluorescent microscopy images
TL;DR: In this paper , a self-supervised semantic segmentation (S4) method was proposed for retinal pigment epithelial (RPE) aging studies, which employs a reconstruction and a pairwise representation loss to make the encoder extract structural information, while creating a morphology loss to produce the segmentation map.
MultiHeadGAN: A Deep Learning Method for Low Contrast Retinal Pigment Epithelium Cells Segmentation in Fluorescent Flatmount Microscopy Images
TL;DR: A semi-supervised deep learning approach to segment low contrast cells from impaired regions in RPE flatmount images and is promising to support large scale cell morphological analyses for RPE aging investigations.
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