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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
編輯Chuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
發行者Springer Verlag
頁面665-672
頁數8
ISBN(列印)9789811391897
DOIs
出版狀態Published - 2019 一月 1
事件23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan
持續時間: 2018 十二月 202018 十二月 22

出版系列

名字Communications in Computer and Information Science
1013
ISSN(列印)1865-0929

Conference

Conference23rd International Computer Symposium, ICS 2018
國家Taiwan
城市Yunlin
期間18-12-2018-12-22

指紋

Histology
Lymphocytes
Neural Networks
Neural networks
Liver
Cell
Neutrophils
Impurities
Inflammation
Acute
Nucleus
Patch
Overlap

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

引用此文

Li, C. T., Chung, P-C., Tsai, H. W., Chow, N-H., & Cheng, K-S. (2019). Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation. 於 C-Y. Chang, C-C. Lin, & H-H. Lin (編輯), New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers (頁 665-672). (Communications in Computer and Information Science; 卷 1013). Springer Verlag. https://doi.org/10.1007/978-981-13-9190-3_73
Li, Chao Ting ; Chung, Pau-Choo ; Tsai, Hung Wen ; Chow, Nan-Haw ; Cheng, Kuo-Sheng. / Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation. New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. 編輯 / Chuan-Yu Chang ; Chien-Chou Lin ; Horng-Horng Lin. Springer Verlag, 2019. 頁 665-672 (Communications in Computer and Information Science).
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title = "Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation",
abstract = "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.",
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Li, CT, Chung, P-C, Tsai, HW, Chow, N-H & Cheng, K-S 2019, Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation. 於 C-Y Chang, C-C Lin & H-H Lin (編輯), New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. Communications in Computer and Information Science, 卷 1013, Springer Verlag, 頁 665-672, 23rd International Computer Symposium, ICS 2018, Yunlin, Taiwan, 18-12-20. https://doi.org/10.1007/978-981-13-9190-3_73

Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation. / Li, Chao Ting; Chung, Pau-Choo; Tsai, Hung Wen; Chow, Nan-Haw; Cheng, Kuo-Sheng.

New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. 編輯 / Chuan-Yu Chang; Chien-Chou Lin; Horng-Horng Lin. Springer Verlag, 2019. p. 665-672 (Communications in Computer and Information Science; 卷 1013).

研究成果: Conference contribution

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AU - Li, Chao Ting

AU - Chung, Pau-Choo

AU - Tsai, Hung Wen

AU - Chow, Nan-Haw

AU - Cheng, Kuo-Sheng

PY - 2019/1/1

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N2 - 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.

AB - 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.

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Li CT, Chung P-C, Tsai HW, Chow N-H, Cheng K-S. Inflammatory Cells Detection in H&E Staining Histology Images Using Deep Convolutional Neural Network with Distance Transformation. 於 Chang C-Y, Lin C-C, Lin H-H, 編輯, New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers. Springer Verlag. 2019. p. 665-672. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-13-9190-3_73