Partnership to Create Database of Annotated Tissue Images

LabPON and Royal Philips are to create a digital database of massive aggregated sets of annotated pathology images and big data utilising Philips IntelliSite Pathology Solution, that will provide pathologists with a wealth of clinical information for the development of image analytics algorithms for computational pathology and pathology education.

As one of the largest pathology laboratories in the Netherlands, LabPON will contribute its repository of approximately 300,000 whole slide images (WSI) it prospectively creates each year to the database. This will contain de-identified datasets of annotated cases that are manually commented by the pathologist and will comprise of a wide variety of tissue and disease types, as well as other pertinent diagnostic information to facilitate deep learning.

“Deep learning focuses on the development of advanced computer programs that automatically understand and digitally map tissue images in considerable detail; the more data available, the more refined the computer analysis will be,” said Peter Hamilton, Group Leader Image Analytics at Philips Digital Pathology Solutions. “Together, LabPON and Philips have the competence and skills to realise this.”

“The role of the pathologist remains important by making the definitive diagnosis, which has a high impact on the patient’s treatment. Software tools could help to relieve part of the pathologists’ work such as identifying tumour cells, counting mitotic cells or identifying perineural and vaso-invasive growth, as well carrying out measurements in a more accurate and precise way,” said Alexi Baidoshvili, pathologist at LabPON. “This ultimately could help to improve the quality of diagnosis and make it more objective.”

Next to the development of computational algorithms for diagnostic use, Philips intends to make available the database to research institutions and other partners through its translational research platform. This could enable selected parties to interrogate and combine massive datasets with the goal to discover new insights that ultimately could be translated into new personalised treatment options for patients.