Grading Cancer from Liver Histology Images Using Inter and Intra Region Spatial Relations

Histology image analysis is widely used in cancer studies since it preserves the tissue structure. In this paper, we propose a framework to grade metastatic liver histology images based on the spatial organization inter and intra regions. After detecting the presence of metastases, we first decompose the image into regions corresponding to the tissue types (sane, cancerous, vessels and gaps). A sample of each type is further decomposed into the contained biological objects (nuclei, stroma, gaps). The spatial relations between all the pairs of regions and objects are measured using a Force Histogram Decomposition. A specimen is described using a Bag of Words model aggregating the features measured on all its randomly acquired images. The grading is made using a Naive Bayes Classifier. Experiments on a 23 mice dataset with CT26 intrasplenic tumors highlight the relevance of the spatial relations with a correct grading rate of 78.95%.