User friendly, cloud based, whole slide image segmentation

Brendon Lutnick; Avinash Kammardi Shashiprakash; David Manthey; Pinaki Sarder

doi:10.1117/12.2581383

15 February 2021 User friendly, cloud based, whole slide image segmentation

Brendon Lutnick, Avinash Kammardi Shashiprakash, David Manthey, Pinaki Sarder

Proceedings Volume 11603, Medical Imaging 2021: Digital Pathology; 116030J (2021) https://doi.org/10.1117/12.2581383
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. We have developed a plugin for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. This plugin runs a state-of-the-art convolutional neural network for segmentation of WSIs in the cloud. Our plugin is built on the open source tool HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data management and viewing abilities for WSI datasets. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Users can easily upload slides to a server where our plugin is installed and perform the segmentation analysis remotely. This plugin is open source and once trained, has the ability to be applied to the segmentation of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, demonstrating it on holdout tissue slides.

Conference Presentation

Citation Download Citation

Brendon Lutnick, Avinash Kammardi Shashiprakash, David Manthey, and Pinaki Sarder "User friendly, cloud based, whole slide image segmentation", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 116030J (15 February 2021); https://doi.org/10.1117/12.2581383

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