Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation

Chao Ting Li, Pau Choo Chung, Hung Wen Tsai, Nan Haw Chow, Kuo Sheng Cheng

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

4 Citations (Scopus)


Inflammatory cells such as lymphocytes and neutrophils are crucial indicators in diagnosing acute inflammation from liver histology images. However, there are several challenges in detecting the inflammatory cells. The inflammatory cells have large variation and also appear similar to other cells. In an often occasion, the inflammatory cells may overlap each other. It is also unavoidable to see the clustery noise in the background. To conquer the above-mentioned problems, this paper proposes a procedure, which implements the detection-then-classification by combining the distance transformation with deep convolutional neural networks for detecting an accurate position of each cell. Then a precise image patch can be extracted for a deep convolutional neural network for classification of the cells into nuclei, lymphocyte, neutrophils and impurity (e.g. Kupffer cell). The experimental results show that the proposed approach can effectively detect the inflammatory cells from H&E Staining liver histopathological images, with an accuracy of 93.7% in inflammatory cells classification.

Original languageEnglish
Title of host publicationNew Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
EditorsChuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9789811391897
Publication statusPublished - 2019
Event23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan
Duration: 2018 Dec 202018 Dec 22

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference23rd International Computer Symposium, ICS 2018

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics


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