Automatic cell counting from stimulated Raman imaging using deep learning.

In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins...

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Autores principales:	Qianqian Zhang, Kyung Keun Yun, Hao Wang, Sang Won Yoon, Fake Lu, Daehan Won
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/77bdde8192a5427592974467cc74c400
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spelling	oai:doaj.org-article:77bdde8192a5427592974467cc74c4002021-12-02T20:06:41ZAutomatic cell counting from stimulated Raman imaging using deep learning.1932-620310.1371/journal.pone.0254586https://doaj.org/article/77bdde8192a5427592974467cc74c4002021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254586https://doaj.org/toc/1932-6203In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins from fresh specimens and conducting a fast disclose of fundamental diagnostic hallmarks of tumors with a high resolution. However, cell counting from label-free SRS images has been challenging due to the limited contrast of cells and tissue, along with the heterogeneity of tissue morphology and biochemical compositions. To this end, a deep learning-based cell counting scheme is proposed by modifying and applying U-Net, an effective medical image semantic segmentation model that uses a small number of training samples. The distance transform and watershed segmentation algorithms are also implemented to yield the cell instance segmentation and cell counting results. By performing cell counting on SRS images of real human brain tumor specimens, promising cell counting results are obtained with > 98% of area under the curve (AUC) and R = 0.97 in terms of cell counting correlation between SRS and histological images with hematoxylin and eosin (H&E) staining. The proposed cell counting scheme illustrates the possibility and potential of performing cell counting automatically in near real time and encourages the study of applying deep learning techniques in biomedical and pathological image analyses.Qianqian ZhangKyung Keun YunHao WangSang Won YoonFake LuDaehan WonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254586 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qianqian Zhang Kyung Keun Yun Hao Wang Sang Won Yoon Fake Lu Daehan Won Automatic cell counting from stimulated Raman imaging using deep learning.
description	In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins from fresh specimens and conducting a fast disclose of fundamental diagnostic hallmarks of tumors with a high resolution. However, cell counting from label-free SRS images has been challenging due to the limited contrast of cells and tissue, along with the heterogeneity of tissue morphology and biochemical compositions. To this end, a deep learning-based cell counting scheme is proposed by modifying and applying U-Net, an effective medical image semantic segmentation model that uses a small number of training samples. The distance transform and watershed segmentation algorithms are also implemented to yield the cell instance segmentation and cell counting results. By performing cell counting on SRS images of real human brain tumor specimens, promising cell counting results are obtained with > 98% of area under the curve (AUC) and R = 0.97 in terms of cell counting correlation between SRS and histological images with hematoxylin and eosin (H&E) staining. The proposed cell counting scheme illustrates the possibility and potential of performing cell counting automatically in near real time and encourages the study of applying deep learning techniques in biomedical and pathological image analyses.
format	article
author	Qianqian Zhang Kyung Keun Yun Hao Wang Sang Won Yoon Fake Lu Daehan Won
author_facet	Qianqian Zhang Kyung Keun Yun Hao Wang Sang Won Yoon Fake Lu Daehan Won
author_sort	Qianqian Zhang
title	Automatic cell counting from stimulated Raman imaging using deep learning.
title_short	Automatic cell counting from stimulated Raman imaging using deep learning.
title_full	Automatic cell counting from stimulated Raman imaging using deep learning.
title_fullStr	Automatic cell counting from stimulated Raman imaging using deep learning.
title_full_unstemmed	Automatic cell counting from stimulated Raman imaging using deep learning.
title_sort	automatic cell counting from stimulated raman imaging using deep learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/77bdde8192a5427592974467cc74c400
work_keys_str_mv	AT qianqianzhang automaticcellcountingfromstimulatedramanimagingusingdeeplearning AT kyungkeunyun automaticcellcountingfromstimulatedramanimagingusingdeeplearning AT haowang automaticcellcountingfromstimulatedramanimagingusingdeeplearning AT sangwonyoon automaticcellcountingfromstimulatedramanimagingusingdeeplearning AT fakelu automaticcellcountingfromstimulatedramanimagingusingdeeplearning AT daehanwon automaticcellcountingfromstimulatedramanimagingusingdeeplearning
_version_	1718375366575259648

Automatic cell counting from stimulated Raman imaging using deep learning.

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