Fig. 2

Representative pictures of digital image analysis (DIA) masks on a high cellular density breast cancer case (a). The first step of analysis with HALO (b) and QuantCenter (d) is the training of machine-learning classification to identify the tissue pattern (in this case areas of tumor cells) to be scored. Then, the cell segmentation is only applied in the annotations designated by the machine-learning classification. Thus, only tumor cells are shown in the DIA masks for both HALO and QuantCenter. Blue indicates negative tumor cells, and yellow, orange, and red indicate 1+, 2+, and 3+ positive tumor cells. In QuPath (c), the order of operations is switched, so that cell segmentation is the first, followed by machine-learning classification to identify a sub-population of cells to be scored (in this case tumor cells). Green indicates stromal cells, purple marks immune cells, blue corresponds to negative tumor cells, and yellow, orange, and red indicate 1+, 2+, and 3+ positive tumor cells