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Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images
Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764590/ https://www.ncbi.nlm.nih.gov/pubmed/33321713 http://dx.doi.org/10.3390/s20247063 |
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author | Jirik, Miroslav Gruber, Ivan Moulisova, Vladimira Schindler, Claudia Cervenkova, Lenka Palek, Richard Rosendorf, Jachym Arlt, Janine Bolek, Lukas Dejmek, Jiri Dahmen, Uta Zelezny, Milos Liska, Vaclav |
author_facet | Jirik, Miroslav Gruber, Ivan Moulisova, Vladimira Schindler, Claudia Cervenkova, Lenka Palek, Richard Rosendorf, Jachym Arlt, Janine Bolek, Lukas Dejmek, Jiri Dahmen, Uta Zelezny, Milos Liska, Vaclav |
author_sort | Jirik, Miroslav |
collection | PubMed |
description | Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy. |
format | Online Article Text |
id | pubmed-7764590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77645902020-12-27 Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images Jirik, Miroslav Gruber, Ivan Moulisova, Vladimira Schindler, Claudia Cervenkova, Lenka Palek, Richard Rosendorf, Jachym Arlt, Janine Bolek, Lukas Dejmek, Jiri Dahmen, Uta Zelezny, Milos Liska, Vaclav Sensors (Basel) Letter Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy. MDPI 2020-12-10 /pmc/articles/PMC7764590/ /pubmed/33321713 http://dx.doi.org/10.3390/s20247063 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Letter Jirik, Miroslav Gruber, Ivan Moulisova, Vladimira Schindler, Claudia Cervenkova, Lenka Palek, Richard Rosendorf, Jachym Arlt, Janine Bolek, Lukas Dejmek, Jiri Dahmen, Uta Zelezny, Milos Liska, Vaclav Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images |
title | Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images |
title_full | Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images |
title_fullStr | Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images |
title_full_unstemmed | Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images |
title_short | Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images |
title_sort | semantic segmentation of intralobular and extralobular tissue from liver scaffold h&e images |
topic | Letter |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764590/ https://www.ncbi.nlm.nih.gov/pubmed/33321713 http://dx.doi.org/10.3390/s20247063 |
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