Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain

Abstract Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information...

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Autores principales:	Hyeongsub Kim, Hongjoon Yoon, Nishant Thakur, Gyoyeon Hwang, Eun Jung Lee, Chulhong Kim, Yosep Chong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe5798371932434ca0c4f8e34117fb84
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spelling	oai:doaj.org-article:fe5798371932434ca0c4f8e34117fb842021-11-21T12:20:05ZDeep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain10.1038/s41598-021-01905-z2045-2322https://doaj.org/article/fe5798371932434ca0c4f8e34117fb842021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01905-zhttps://doaj.org/toc/2045-2322Abstract Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.Hyeongsub KimHongjoon YoonNishant ThakurGyoyeon HwangEun Jung LeeChulhong KimYosep ChongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hyeongsub Kim Hongjoon Yoon Nishant Thakur Gyoyeon Hwang Eun Jung Lee Chulhong Kim Yosep Chong Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
description	Abstract Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.
format	article
author	Hyeongsub Kim Hongjoon Yoon Nishant Thakur Gyoyeon Hwang Eun Jung Lee Chulhong Kim Yosep Chong
author_facet	Hyeongsub Kim Hongjoon Yoon Nishant Thakur Gyoyeon Hwang Eun Jung Lee Chulhong Kim Yosep Chong
author_sort	Hyeongsub Kim
title	Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
title_short	Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
title_full	Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
title_fullStr	Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
title_full_unstemmed	Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
title_sort	deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fe5798371932434ca0c4f8e34117fb84
work_keys_str_mv	AT hyeongsubkim deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain AT hongjoonyoon deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain AT nishantthakur deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain AT gyoyeonhwang deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain AT eunjunglee deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain AT chulhongkim deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain AT yosepchong deeplearningbasedhistopathologicalsegmentationforwholeslideimagesofcolorectalcancerinacompresseddomain
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Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain

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