Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 1  |  Page : 9

Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images


Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA

Correspondence Address:
Dr. Peter J. Schüffler
Memorial Sloan Kettering Cancer Center, 417E 68th St, New York City, NY 10065
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_85_20

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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.


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