Open AccessDeep Learning Nuclei Detection in Digitized Histology Images by Superpixels
Author(s) -
Sudhir Sornapudi,
Ronald J Stanley,
William V. Stoecker,
Haidar Almubarak,
L. Rodney Long,
Sameer Antani,
George R. Thoma,
Rosemary E. Zuna,
Shelliane R. Frazier
Publication year - 2018
Publication title -
journal of pathology informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.009
H-Index - 17
ISSN - 2153-3539
DOI - 10.4103/jpi.jpi_74_17
Subject(s) - artificial intelligence , computer science , digital pathology , benchmark (surveying) , pattern recognition (psychology) , segmentation , convolutional neural network , deep learning , cluster analysis , image segmentation , computer vision , geodesy , geography
The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom