Accuracy of the watershed cell segmentation.

<p>Accuracy of the watershed cell segmentation.</p

A Flowchart showing the single steps of our methodology.

<p>After obtaining the images, pre-processing steps enhance the image quality and watershed segmentation for the subsequent segmentation is applied. Accordingly the cell graphs are generated and features are computerized. The last step uses a SVM to classify the graphs as either tumor or stroma.</p

Graph metrics used to train the classifier and their description.

<p>Graph metrics used to train the classifier and their description.</p

The different image processing steps and the graph generation steps.

<p>(<b>a</b>) original image of the DAPI-channel; (<b>b</b>) image after shading correction and noise removal; (<b>c</b>) result of the watershed segmentation, the segmented cells are highlighted by green contour; (<b>d</b>) the image after removal of single cells; (<b>e</b>) showing the cells which were connected via the graph generation step in the same color (cells marked with the same color belong to the same sub-graph); (<b>f</b>) cell graph representation of the cells. The red dots are the nodes which represent the cells, the black lines are the edges between them.</p

The average classification accuracies.

<p>The table shows the accuracies of the training set and the accuracies of the slides from the test set.</p

Conceptional representation of cell graphs.

<p>(<b>a</b>) Artificial sketch of 3 different 3 cell type: tumor cells in blue, lymphocytes in white and in purple fibroblast. (<b>b</b>) Cell graph representation of (a). Cells are depicted as nodes and links between them represent biological relations.</p

The F-scores of each feature in descending order.

<p>The table shows the evaluated features sorted by their decreasing value for tissue classification (F-score). For each feature it is given whether it is of morphological (M), intensity (I), and topological character (T).</p

Conglomerates and single cells.

<p>Liver metastasis of colorectal cancer with strong T cell infiltrate (CD3 staining: dark red with hematoxylin counterstaining, A: overview, digital magnification 10×, B: conglomerate (magnification 40×), C: single cells, (magnification 40×).</p

Rationale behind the presented approach.

<p>The figure shows the relation of the size of T cells to the height of the used tissue sections (2 µm). The sections are so thin, that there is only minimal overlap between the individual cells of a conglomerate. This allows for calculating the number of cells in a conglomerate by its total area.</p

The results of the classification.

<p>(a–d) showing the original RGB core images; (e–h) showing the corresponding DAPI channel as an intensity image of the cores (a–d); (i–l) results of the classification step, green = cells classified as tumor cells, blue = cells classified as stroma cells.</p