Oslo Universitetssykehus

Room4 Group Limited

Oslo Universitetssykehus, Institute for Cancer Genetics and Informatics

Oxford Cancer Biomarkers Ltd

John Robert Maddison

Havard E Danielsen

Tarjei Sveinsgjerd Hveem

Havard Emil Greger Danielsen

Ole Johan Skrede

Knut Liestol

David Kerr

Rolf Anders Syvertsen

Håvard Danielsen

A machine learning algorithm is trained on a number of microscopic images and a measure of outcome of each image. Each image is divided into tiles. The measure of outcome is assigned to each tile of the image. The tiles are then used to train the machine learning algorithm. The trained algorithm may then be used to evaluate images.

Histological image analysis

An apparatus and computer-implemented method for training a machine-learning algorithm to perform histopathological analysis is disclosed. The method comprises obtaining () a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining () a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training () the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. Also disclosed is an apparatus and computer-implemented method for histopathological analysis using the trained machine-learning algorithm.

Histopathological image analysis

A method of image classification is used for classifying images of stained cell nuclei. For each nucleus, the total integrated optical density is calculated and a histogram calculated. The image is then classified by automatically identifying peaks, identifying the lowest peak as a 2C peak and classifying the image as at least one of diploid, tetraploid, aneuploid or polyploid based on the number of peaks and the count at an integrated optical densities above the 2C peak.

Automatic calculation for ploidy classification

Quantitative analysis of a tissue sample includes carrying out a ploidy measurement on a plurality of nuclei of the sample to determine the ploidy type of a sample and also carrying out a stroma measurement on a section to determine the stroma type of a sample. The ploidy type, e.g. diploid or non-diploid, and the stroma type, high or low stroma, give an improved patient survival estimate than the ploidy type alone.

Tissue sample analysis technique

A method and apparatus for aligning microscope images. Microscope images of the same or very similar subjects provided by different microscopes are aligned. The images from two types of microscope, such as a Virtual Microscope (VM) and a Light Microscope (LM) are used. An image produced by the virtual microscope is easily read by a viewer, as it represents a scan of a whole slide rather than individual high power fields of view. An area of the image can be selected for further examination or objective analysis by the LM microscope. The qualitative or quantitative information obtained from the Light microscope using the method described may then be located back into the virtual microscope image to provide understandable context.

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Crowborough, United Kingdom

John Robert Maddison