Koninklijke Philips Corporation N.v.

Marc Van Driel

Anke Pierik

Reinhold Wimberger-Friedl

Dmitry Nikolayevich Znamenskiy

Fei Zuo

Koen De Laat

Kamana Sigdel

Reinhold Wimberger-friedl

Currently, there is interest in applying machine learning techniques to analyse digital pathology images automatically. Machine learning techniques often rely on training with a ground-truth image input. The quality and amount of training data determines the quality of the detector, as expressed in the rate of true and false positives, and robustness against variations in the appearance of the input images. The present application proposes to obtain image data of the same sample before and after at least one re-staining step (firstly with a structure-revealing stain, and secondly with a bio marker revealing stain). Sections of the first and second image data having a good registration relationship are chosen, along with the probability of detecting a desired candidate object (such as nucleus) and the probability of the bio marker revealing stain being present annotation data suitable for training a machine learning algorithm on the first and/or the second image data is provided.

Generating annotation data of tissue images

A system and method are provided which use a machine learning algorithm to obtain a learned annotation of objects in one or more scales of a multiscale image. A viewing window () is provided for viewing the multiscale image. The viewing window is configurable on the basis of a magnification factor, which selects one of the plurality of scales for viewing, and a spatial offset parameter. A user may provide a manual annotation of an object in the viewing window, which is then used as training feedback in the learning of the machine learning algorithm. To enable the user to more effectively provide the manual annotation, the magnification factor and the spatial offset parameter for the viewing window may be automatically determined, namely by the system and method determining where in the multiscale image the manual annotation of the object would have sufficient influence on the learned annotation provided by the machine learning algorithm. The determined influence may be shown in the form of an overlay () in the viewing window.

Learning annotation of objects in image

A method is presented that enables the spatial mapping of nucleic acids of tissue samples with high resolution and without sacrificing the degree of multiplexing that is available from next-generation sequencing. The method is based on the application of patterns of barcoded oligonucleotides probes onto predefined locations in a region of interest in a tissue sample. Every nucleic acid analyzed can be allocated to a certain position inside the sample based on the barcode. Various printing technologies can be used and different ways of patterning can be employed, like a regular array with a certain pitch or alternatively an object-based patterning with defined regions of interest without shape constraints.

Spatial mapping of molecular profiles of biological tissue samples

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Eindhoven, Netherlands

Marc Van Driel