The data generated by the omics and imaging technologies do not lend themselves to immediate incorporation in computational models of cancer but must be pre-processed or in some cases even extracted from the raw data.
Advances in image processing and computer vision nowadays allow the automated extraction of features from radiology and pathology images. While automated segmentation of radiology images cannot replace manual annotation by doctors, it can help them to delineate the three-dimensional shape of tumours efficiently. Similarly, automated algorithms are far from the reliability and expertise level of human pathologists, but they are already used to extract simple features from digital pathology images such as cell counts, biomarker quantification or basic morphological descriptors. The advantage of these automated software, is that, provided sufficient computational resources, they can process large areas the images. They are also unaffected by the biases linked to inter-observer variability.
