Engineering the future of 3D pathology
In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole...
| Autors principals: | , , , , , , , , , , |
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| Format: | Journal article |
| Idioma: | English |
| Publicat: |
Wiley
2023
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| _version_ | 1826315371904237568 |
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| author | Liu, JT Chow, SS Colling, R Downes, MR Farré, X Humphrey, P Janowczyk, A Mirtti, T Verrill, C Zlobec, I True, LD |
| author_facet | Liu, JT Chow, SS Colling, R Downes, MR Farré, X Humphrey, P Janowczyk, A Mirtti, T Verrill, C Zlobec, I True, LD |
| author_sort | Liu, JT |
| collection | OXFORD |
| description | In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets. |
| first_indexed | 2024-12-09T03:24:55Z |
| format | Journal article |
| id | oxford-uuid:b64ab81f-562a-4d0e-b900-ebb1ec6b965d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:24:55Z |
| publishDate | 2023 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:b64ab81f-562a-4d0e-b900-ebb1ec6b965d2024-11-27T15:51:08ZEngineering the future of 3D pathologyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b64ab81f-562a-4d0e-b900-ebb1ec6b965dEnglishSymplectic ElementsWiley2023Liu, JTChow, SSColling, RDownes, MRFarré, XHumphrey, PJanowczyk, AMirtti, TVerrill, CZlobec, ITrue, LDIn recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets. |
| spellingShingle | Liu, JT Chow, SS Colling, R Downes, MR Farré, X Humphrey, P Janowczyk, A Mirtti, T Verrill, C Zlobec, I True, LD Engineering the future of 3D pathology |
| title | Engineering the future of 3D pathology |
| title_full | Engineering the future of 3D pathology |
| title_fullStr | Engineering the future of 3D pathology |
| title_full_unstemmed | Engineering the future of 3D pathology |
| title_short | Engineering the future of 3D pathology |
| title_sort | engineering the future of 3d pathology |
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