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Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy...

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Autores principales: Zováthi, Bendegúz H., Mohácsi, Réka, Szász, Attila Marcell, Cserey, György
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498155/
https://www.ncbi.nlm.nih.gov/pubmed/36140562
http://dx.doi.org/10.3390/diagnostics12092161
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author Zováthi, Bendegúz H.
Mohácsi, Réka
Szász, Attila Marcell
Cserey, György
author_facet Zováthi, Bendegúz H.
Mohácsi, Réka
Szász, Attila Marcell
Cserey, György
author_sort Zováthi, Bendegúz H.
collection PubMed
description In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.
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spelling pubmed-94981552022-09-23 Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network Zováthi, Bendegúz H. Mohácsi, Réka Szász, Attila Marcell Cserey, György Diagnostics (Basel) Article In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before. MDPI 2022-09-06 /pmc/articles/PMC9498155/ /pubmed/36140562 http://dx.doi.org/10.3390/diagnostics12092161 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zováthi, Bendegúz H.
Mohácsi, Réka
Szász, Attila Marcell
Cserey, György
Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
title Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
title_full Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
title_fullStr Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
title_full_unstemmed Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
title_short Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
title_sort breast tumor tissue segmentation with area-based annotation using convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498155/
https://www.ncbi.nlm.nih.gov/pubmed/36140562
http://dx.doi.org/10.3390/diagnostics12092161
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