Cell nuclei detection and segmentation for computational pathology using deep learning

Kemeng Chen, Ning Zhang, Linda Powers, Janet Roveda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This work presents a deep learning model and image processing based processing flow to detect and segment nuclei from microscopy images. This work aims at isolating each nuclei by segmenting the boundary and detecting the geometric center of the nuclei. The deep learning model employs a multi-layer convolutional neural network based architecture to extract features from both spatial and color information and to generate a gray scaled image mask. Subsequent image processing steps smooth nuclei boundaries, isolate each individual nuclei and calculate the geometric center of the nuclei. The proposed work has been implemented and tested using H E stained microscopy images containing seven different tissue samples. Experimental results demonstrated an average precision of 0.799, recall of 0.955, F-score of 0.86, and IoU of 0.835.

Original languageEnglish (US)
Title of host publication2019 Spring Simulation Conference, SpringSim 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781510883888
DOIs
StatePublished - Apr 2019
Event2019 Spring Simulation Conference, SpringSim 2019 - Tucson, United States
Duration: Apr 29 2019May 2 2019

Publication series

Name2019 Spring Simulation Conference, SpringSim 2019

Conference

Conference2019 Spring Simulation Conference, SpringSim 2019
CountryUnited States
CityTucson
Period4/29/195/2/19

Keywords

  • Deep learning
  • Detection
  • Image processing
  • Nuclei
  • Segmentation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'Cell nuclei detection and segmentation for computational pathology using deep learning'. Together they form a unique fingerprint.

Cite this