Accuracy of the watershed cell segmentation.

<p>Accuracy of the watershed cell segmentation.</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 detailed steps for whole-slide sharpness quantification;

<p>At first, the slide is divided into 16 sub-regions. Then, cells are detected by their color values. In total 200 cells are used to quantify the sharpness of each region. For every cell, five sharpness features are computed and a support vector machine (SVM) is used to classify each cell into the in-focus (class 1) or out-of-focus(class 0) category. The percentage of in-focus cells (0–100%) is used to calculate a score for each region, and a combination of these scores is used to represent slide sharpness.</p

Highly detailed single-slide analysis.

<p>(a) A schematic showing the origin of the optical <i>z</i>-axis; Red arrow: showing the measured distance from the objective to the measured objects. (b) A 3D graph of the focus points of two different layers which can be found on the slides. The red dots represent points focused on dust which are located on the coverslip. The blue dots are the focus points of the cell layer. The graph looks inverse comparing to the real physical location of the focus points as its origin lies in the lower left corner; (c) a 3D mesh plot of the obtained focus point data only by the cell layer of the slide shows a high degree of heterogeneity within the slides; (d) another example similar to (c) in which smaller variations in the <i>z</i> values were observed. The examples in (c) and (d) demonstrate that it is not possible to scan the slides as a planar mono-layer and that there is a high height variation within the slides.</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

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

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

Descriptive statistics of the focus point dataset of the particular slides showing the high variations between the z-values within and between the slides.

<p>Descriptive statistics of the focus point dataset of the particular slides showing the high variations between the z-values within and between the slides.</p

Semantic Focusing Allows Fully Automated Single-Layer Slide Scanning of Cervical Cytology Slides

<div><p>Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.</p></div