Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images
Background: The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists ro...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-01-01
|
| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2021;volume=12;issue=1;spage=9;epage=9;aulast=Schuffler |
| _version_ | 1817977790967840768 |
|---|---|
| author | Peter J. Schüffler Dig Vijay Kumar Yarlagadda Chad Vanderbilt Thomas J Fuchs |
| author_facet | Peter J. Schüffler Dig Vijay Kumar Yarlagadda Chad Vanderbilt Thomas J Fuchs |
| author_sort | Peter J. Schüffler |
| collection | DOAJ |
| description | Background: The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods: We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data. |
| first_indexed | 2024-04-13T22:21:12Z |
| format | Article |
| id | doaj.art-78156a9498fa41c8ab70daf9b2cd40e9 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 2153-3539 |
| language | English |
| last_indexed | 2024-04-13T22:21:12Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-78156a9498fa41c8ab70daf9b2cd40e92022-12-22T02:27:14ZengElsevierJournal of Pathology Informatics2153-35392153-35392021-01-011219910.4103/jpi.jpi_85_20Overcoming an annotation hurdle: Digitizing pen annotations from whole slide imagesPeter J. SchüfflerDig Vijay Kumar YarlagaddaChad VanderbiltThomas J FuchsBackground: The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods: We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2021;volume=12;issue=1;spage=9;epage=9;aulast=Schufflercomputational pathologydigital pathologypen annotationstraining data generation |
| spellingShingle | Peter J. Schüffler Dig Vijay Kumar Yarlagadda Chad Vanderbilt Thomas J Fuchs Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images Journal of Pathology Informatics computational pathology digital pathology pen annotations training data generation |
| title | Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images |
| title_full | Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images |
| title_fullStr | Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images |
| title_full_unstemmed | Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images |
| title_short | Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images |
| title_sort | overcoming an annotation hurdle digitizing pen annotations from whole slide images |
| topic | computational pathology digital pathology pen annotations training data generation |
| url | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2021;volume=12;issue=1;spage=9;epage=9;aulast=Schuffler |
| work_keys_str_mv | AT peterjschuffler overcominganannotationhurdledigitizingpenannotationsfromwholeslideimages AT digvijaykumaryarlagadda overcominganannotationhurdledigitizingpenannotationsfromwholeslideimages AT chadvanderbilt overcominganannotationhurdledigitizingpenannotationsfromwholeslideimages AT thomasjfuchs overcominganannotationhurdledigitizingpenannotationsfromwholeslideimages |