Abstract: Computer-implemented process on an image of a biological sample The present invention relates to a computer-implemented process to automatically analyze a digital image (103) of abiological sample (101). The process involves a change (203) from a first color space to a second color space. Then, fits are performed taking into account several dimensions of the second color space to classify pixels.

Abstract: We present a new method to automatically sample the tumour/stroma interface zone (IZ) from microscopy image analysis data. It first delineates the tumour edge using a set of explicit rules in grid-subsampled tissue areas; then the IZ of controlled width is sampled and ranked by the distance from the edge to compute TIL density profiles across the IZ. From this data, a set of novel Immunogradient indicators are computed to reflect TIL “gravitation” towards the tumour. We applied the method on CD8 immunohistochemistry images of surgically excised breast and colorectal cancers to predict overall patient survival. In both patient cohorts, we found strong and independent prognostic value of the Immunogradient indicators, outperforming methods currently available. We conclude that data-driven, automated, human operator-independent IZ sampling enables precise spatial immune response measurement in the tumour/host interaction frontline for prediction of disease and therapy outcomes.

Abstract: Computer-implemented process on an image of a biological sample The present invention relates to a computer-implemented process to automatically analyze a digital image (103) of abiological sample (101). The process involves a change (203) from a first color space to a second color space. Then, fits are performed taking into account several dimensions of the second color space to classify pixels.