An Unsupervised Parameter-Free Nuclei Segmentation Method for Histology Images
Author:
Affiliation:
1. University of Southern California,Los Angeles,California,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897887.pdf?arnumber=9897887
Reference24 articles.
1. Robust Histopathology Image Analysis: To Label or to Synthesize?
2. BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images
3. Crowd-sourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd;irshad;Pacific symposium on bio-computing Co-chairs,2014
4. Fiji: an open-source platform for biological-image analysis
5. NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images
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