Lymphocyte Classification from Hoechst Stained Slides with Deep Learning
Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify proteins expressed on the surface of cells. This enables cell classification, better understanding of the tumour microenvironment, and more accurate diagnoses, prognoses, and tailored i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/23/5957 |
| _version_ | 1797463460112498688 |
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| author | Jessica Cooper In Hwa Um Ognjen Arandjelović David J. Harrison |
| author_facet | Jessica Cooper In Hwa Um Ognjen Arandjelović David J. Harrison |
| author_sort | Jessica Cooper |
| collection | DOAJ |
| description | Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify proteins expressed on the surface of cells. This enables cell classification, better understanding of the tumour microenvironment, and more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, these techniques are expensive. They are time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is far cheaper and easier to perform, but is not typically used as it binds to DNA rather than to the proteins targeted by immunofluorescence techniques. In this work we show that through the use of deep learning it is possible to identify an immune cell subtype without immunofluorescence. We train a deep convolutional neural network to identify cells expressing the T lymphocyte marker CD3 from Hoechst 33342 stained tissue only. CD3 expressing cells are often used in key prognostic metrics such as assessment of immune cell infiltration, and by identifying them without the need for costly immunofluorescence, we present a promising new approach to cheaper prediction and improvement of patient outcomes. We also show that by using deep learning interpretability techniques, we can gain insight into the previously unknown morphological features which make this possible. |
| first_indexed | 2024-03-09T17:51:01Z |
| format | Article |
| id | doaj.art-0abd7471cd484367915994a7aa057dcf |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T17:51:01Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-0abd7471cd484367915994a7aa057dcf2023-11-24T10:41:27ZengMDPI AGCancers2072-66942022-12-011423595710.3390/cancers14235957Lymphocyte Classification from Hoechst Stained Slides with Deep LearningJessica Cooper0In Hwa Um1Ognjen Arandjelović2David J. Harrison3School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UKSchool of Medicine, University of St Andrews, St Andrews KY16 9TF, UKSchool of Computer Science, University of St Andrews, St Andrews KY16 9SX, UKSchool of Medicine, University of St Andrews, St Andrews KY16 9TF, UKMultiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify proteins expressed on the surface of cells. This enables cell classification, better understanding of the tumour microenvironment, and more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, these techniques are expensive. They are time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is far cheaper and easier to perform, but is not typically used as it binds to DNA rather than to the proteins targeted by immunofluorescence techniques. In this work we show that through the use of deep learning it is possible to identify an immune cell subtype without immunofluorescence. We train a deep convolutional neural network to identify cells expressing the T lymphocyte marker CD3 from Hoechst 33342 stained tissue only. CD3 expressing cells are often used in key prognostic metrics such as assessment of immune cell infiltration, and by identifying them without the need for costly immunofluorescence, we present a promising new approach to cheaper prediction and improvement of patient outcomes. We also show that by using deep learning interpretability techniques, we can gain insight into the previously unknown morphological features which make this possible.https://www.mdpi.com/2072-6694/14/23/5957deep learningcomputer visionlymphocyte subsetsimage classificationimaging |
| spellingShingle | Jessica Cooper In Hwa Um Ognjen Arandjelović David J. Harrison Lymphocyte Classification from Hoechst Stained Slides with Deep Learning Cancers deep learning computer vision lymphocyte subsets image classification imaging |
| title | Lymphocyte Classification from Hoechst Stained Slides with Deep Learning |
| title_full | Lymphocyte Classification from Hoechst Stained Slides with Deep Learning |
| title_fullStr | Lymphocyte Classification from Hoechst Stained Slides with Deep Learning |
| title_full_unstemmed | Lymphocyte Classification from Hoechst Stained Slides with Deep Learning |
| title_short | Lymphocyte Classification from Hoechst Stained Slides with Deep Learning |
| title_sort | lymphocyte classification from hoechst stained slides with deep learning |
| topic | deep learning computer vision lymphocyte subsets image classification imaging |
| url | https://www.mdpi.com/2072-6694/14/23/5957 |
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