Papillary thyroid carcinoma whole-slide images as a basis for deep learning
Objectives. Morphological analysis of papillary thyroid cancer is a cornerstone for further treatment planning. Traditional and neural network methods of extracting parts of images are used to automate the analysis. It is necessary to prepare a set of data for teaching neural networks to develop a s...
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| Format: | Article |
| Language: | Russian |
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National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2023-06-01
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| Series: | Informatika |
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| Online Access: | https://inf.grid.by/jour/article/view/1241 |
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| author | M. V. Fridman A. A. Kosareva E. V. Snezhko P. V. Kamlach V. A. Kovalev |
| author_facet | M. V. Fridman A. A. Kosareva E. V. Snezhko P. V. Kamlach V. A. Kovalev |
| author_sort | M. V. Fridman |
| collection | DOAJ |
| description | Objectives. Morphological analysis of papillary thyroid cancer is a cornerstone for further treatment planning. Traditional and neural network methods of extracting parts of images are used to automate the analysis. It is necessary to prepare a set of data for teaching neural networks to develop a system of similar anatomical region in the histopathological image. Authors discuss the second selection of signs for the marking of histological images, methodological approaches to dissect whole-slide images, how to prepare raw data for a future analysis. The influence of the representative size of the fragment of the full-to-suction image of papillary thyroid cancer on the accuracy of the classification of trained neural network EfficientNetB0 is conducted. The analysis of the resulting results is carried out, the weaknesses of the use of fragments of images of different representative size and the cause of the unsatisfactory accuracy of the classification on large increase are evaluated.Materials and methods. Histopathological whole-slide imaged of 129 patients were used. Histological micropreparations containing elements of a tumor and surrounding tissue were scanned in the Aperio AT2 (Leica Biosystems, Germany) apparatus with maximum resolution. The marking was carried out in the ASAP software package. To choose the optimal representative size of the fragment the problem of classification was solved using the pre-study neural network EfficientNetB0.Results. A methodology for preparing a database of histopathological images of papillary thyroid cancer was proposed. Experiments were conducted to determine the optimal representative size of the image fragment. The best result of the accuracy of determining the class of test sample showed the size of a representative fragment as 394.32×394.32 microns.Conclusion. The analysis of the influence of the representative sizes of fragments of histopathological images showed the problems in solving the classification tasks because of cutting and staining images specifics, morphological complex and textured differences in the images of the same class. At the same time, it was determined that the task of preparing a set of data for training neural network to solve the problem of finding invasion of vessels in a histopathological image is not trivial and it requires additional stages of data preparation. |
| format | Article |
| id | doaj-art-bfac95fe845d43d9a7f0db9b6029a3d2 |
| institution | DOAJ |
| issn | 1816-0301 |
| language | Russian |
| publishDate | 2023-06-01 |
| publisher | National Academy of Sciences of Belarus, the United Institute of Informatics Problems |
| record_format | Article |
| series | Informatika |
| spelling | doaj-art-bfac95fe845d43d9a7f0db9b6029a3d22025-08-20T03:02:37ZrusNational Academy of Sciences of Belarus, the United Institute of Informatics ProblemsInformatika1816-03012023-06-01202283810.37661/1816-0301-2023-20-2-28-381034Papillary thyroid carcinoma whole-slide images as a basis for deep learningM. V. Fridman0A. A. Kosareva1E. V. Snezhko2P. V. Kamlach3V. A. Kovalev4Minsk City Clinical Oncology Center; Republican Centre for Thyroid TumoursBelarusian State University of Informatics and RadioelectronicsThe United Institute of Informatics Problems of the National Academy of Sciences of BelarusBelarusian State University of Informatics and RadioelectronicsThe United Institute of Informatics Problems of the National Academy of Sciences of BelarusObjectives. Morphological analysis of papillary thyroid cancer is a cornerstone for further treatment planning. Traditional and neural network methods of extracting parts of images are used to automate the analysis. It is necessary to prepare a set of data for teaching neural networks to develop a system of similar anatomical region in the histopathological image. Authors discuss the second selection of signs for the marking of histological images, methodological approaches to dissect whole-slide images, how to prepare raw data for a future analysis. The influence of the representative size of the fragment of the full-to-suction image of papillary thyroid cancer on the accuracy of the classification of trained neural network EfficientNetB0 is conducted. The analysis of the resulting results is carried out, the weaknesses of the use of fragments of images of different representative size and the cause of the unsatisfactory accuracy of the classification on large increase are evaluated.Materials and methods. Histopathological whole-slide imaged of 129 patients were used. Histological micropreparations containing elements of a tumor and surrounding tissue were scanned in the Aperio AT2 (Leica Biosystems, Germany) apparatus with maximum resolution. The marking was carried out in the ASAP software package. To choose the optimal representative size of the fragment the problem of classification was solved using the pre-study neural network EfficientNetB0.Results. A methodology for preparing a database of histopathological images of papillary thyroid cancer was proposed. Experiments were conducted to determine the optimal representative size of the image fragment. The best result of the accuracy of determining the class of test sample showed the size of a representative fragment as 394.32×394.32 microns.Conclusion. The analysis of the influence of the representative sizes of fragments of histopathological images showed the problems in solving the classification tasks because of cutting and staining images specifics, morphological complex and textured differences in the images of the same class. At the same time, it was determined that the task of preparing a set of data for training neural network to solve the problem of finding invasion of vessels in a histopathological image is not trivial and it requires additional stages of data preparation.https://inf.grid.by/jour/article/view/1241medical imagingconvolutional neural networkdeep learningcomputer-aided diagnosispapillary thyroid cancercancer architectonics |
| spellingShingle | M. V. Fridman A. A. Kosareva E. V. Snezhko P. V. Kamlach V. A. Kovalev Papillary thyroid carcinoma whole-slide images as a basis for deep learning Informatika medical imaging convolutional neural network deep learning computer-aided diagnosis papillary thyroid cancer cancer architectonics |
| title | Papillary thyroid carcinoma whole-slide images as a basis for deep learning |
| title_full | Papillary thyroid carcinoma whole-slide images as a basis for deep learning |
| title_fullStr | Papillary thyroid carcinoma whole-slide images as a basis for deep learning |
| title_full_unstemmed | Papillary thyroid carcinoma whole-slide images as a basis for deep learning |
| title_short | Papillary thyroid carcinoma whole-slide images as a basis for deep learning |
| title_sort | papillary thyroid carcinoma whole slide images as a basis for deep learning |
| topic | medical imaging convolutional neural network deep learning computer-aided diagnosis papillary thyroid cancer cancer architectonics |
| url | https://inf.grid.by/jour/article/view/1241 |
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