PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images

The incidence of breast cancer is tending younger globally, and tumor development, clinical treatment, and prognosis are largely influenced by histopathological diagnosis. For diagnosed patients, the distinction between the cancer nests and normal tissue is the basis of breast cancer treatment. Micr...
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Autor*in:	Jiansheng Wang [verfasserIn] Yan Wang [verfasserIn] Xiang Tao [verfasserIn] Qingli Li [verfasserIn] Li Sun [verfasserIn] Jiangang Chen [verfasserIn] Mei Zhou [verfasserIn] Menghan Hu [verfasserIn] Xiufeng Zhou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Microscopic hyperspectral imaging Breast cancer Tissue microarrays Deep learning

Übergeordnetes Werk:	In: Fundamental Research - KeAi Communications Co. Ltd., 2021, 1(2021), 5, Seite 631-640
Übergeordnetes Werk:	volume:1 ; year:2021 ; number:5 ; pages:631-640

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.fmre.2021.06.013

Katalog-ID:	DOAJ015631834

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language	English
source	In Fundamental Research 1(2021), 5, Seite 631-640 volume:1 year:2021 number:5 pages:631-640
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topic_facet	Microscopic hyperspectral imaging Breast cancer Tissue microarrays Deep learning Science (General)
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authorswithroles_txt_mv	Jiansheng Wang @@aut@@ Yan Wang @@aut@@ Xiang Tao @@aut@@ Qingli Li @@aut@@ Li Sun @@aut@@ Jiangang Chen @@aut@@ Mei Zhou @@aut@@ Menghan Hu @@aut@@ Xiufeng Zhou @@aut@@
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callnumber-first	Q - Science
author	Jiansheng Wang
spellingShingle	Jiansheng Wang misc Q1-390 misc Microscopic hyperspectral imaging misc Breast cancer misc Tissue microarrays misc Deep learning misc Science (General) PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images
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topic_title	Q1-390 PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images Microscopic hyperspectral imaging Breast cancer Tissue microarrays Deep learning
topic	misc Q1-390 misc Microscopic hyperspectral imaging misc Breast cancer misc Tissue microarrays misc Deep learning misc Science (General)
topic_unstemmed	misc Q1-390 misc Microscopic hyperspectral imaging misc Breast cancer misc Tissue microarrays misc Deep learning misc Science (General)
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title	PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images
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title_sort	pca-u-net based breast cancer nest segmentation from microarray hyperspectral images
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title_auth	PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images
abstract	The incidence of breast cancer is tending younger globally, and tumor development, clinical treatment, and prognosis are largely influenced by histopathological diagnosis. For diagnosed patients, the distinction between the cancer nests and normal tissue is the basis of breast cancer treatment. Microscopic hyperspectral imaging technology has shown its potential in auxiliary pathological examinations due to the superior imaging modality and data characteristics. This paper presents a method for cancer nest segmentation from hyperspectral images of breast cancer tissue microarray samples. The scheme combines the strengths of the U-Net neural network and unsupervised principal component analysis, which reduces the amount of calculation and improves the recognition accuracy. The experimental accuracy of cancer nest segmentation reaches 87.14%. Furthermore, a set of quantitative pathological characteristic parameters reflects the degree of breast cancer lesions from multiple angles, providing a relatively comprehensive reference for the pathologist's diagnosis. In-depth exploration of the combined development of deep learning and microscopic hyperspectral imaging technology is worthy to promote efficient diagnosis of breast tumors and concern for human health.
abstractGer	The incidence of breast cancer is tending younger globally, and tumor development, clinical treatment, and prognosis are largely influenced by histopathological diagnosis. For diagnosed patients, the distinction between the cancer nests and normal tissue is the basis of breast cancer treatment. Microscopic hyperspectral imaging technology has shown its potential in auxiliary pathological examinations due to the superior imaging modality and data characteristics. This paper presents a method for cancer nest segmentation from hyperspectral images of breast cancer tissue microarray samples. The scheme combines the strengths of the U-Net neural network and unsupervised principal component analysis, which reduces the amount of calculation and improves the recognition accuracy. The experimental accuracy of cancer nest segmentation reaches 87.14%. Furthermore, a set of quantitative pathological characteristic parameters reflects the degree of breast cancer lesions from multiple angles, providing a relatively comprehensive reference for the pathologist's diagnosis. In-depth exploration of the combined development of deep learning and microscopic hyperspectral imaging technology is worthy to promote efficient diagnosis of breast tumors and concern for human health.
abstract_unstemmed	The incidence of breast cancer is tending younger globally, and tumor development, clinical treatment, and prognosis are largely influenced by histopathological diagnosis. For diagnosed patients, the distinction between the cancer nests and normal tissue is the basis of breast cancer treatment. Microscopic hyperspectral imaging technology has shown its potential in auxiliary pathological examinations due to the superior imaging modality and data characteristics. This paper presents a method for cancer nest segmentation from hyperspectral images of breast cancer tissue microarray samples. The scheme combines the strengths of the U-Net neural network and unsupervised principal component analysis, which reduces the amount of calculation and improves the recognition accuracy. The experimental accuracy of cancer nest segmentation reaches 87.14%. Furthermore, a set of quantitative pathological characteristic parameters reflects the degree of breast cancer lesions from multiple angles, providing a relatively comprehensive reference for the pathologist's diagnosis. In-depth exploration of the combined development of deep learning and microscopic hyperspectral imaging technology is worthy to promote efficient diagnosis of breast tumors and concern for human health.
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title_short	PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images
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PCA-U-Net based breast cancer nest segmentation from microarray hyperspectral images

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