A multiview boosting approach to tissue segmentation

Digitized histopathology images have a great potential for improving or facilitating current assessment tools in cancer pathology. In order to develop accurate and robust automated methods, the precise segmentation of histologic objects such epithelium, stroma, and nucleus is necessary, in the hopes of information extraction not otherwise obvious to the subjective eye. Here, we propose a multivew boosting approach to segment histology objects of prostate tissue. Tissue specimen images are first represented at different scales using a Gaussian kernel and converted into several forms such HSV and Lab. Intensity-and texture-based features are extracted from the converted images. Adopting multiview boosting approach, we effectively learn a classifier to predict the histologic class of a pixel in a prostate tissue specimen. The method attempts to integrate the information from multiple scales (or views). 18 prostate tissue specimens from 4 patients were employed to evaluate the new method. The method was trained on 11 tissue specimens including 75,832 epithelial and 103,453 stroma pixels and tested on 55,319 epithelial and 74,945 stroma pixels from 7 tissue specimens. The technique showed 96.7% accuracy, and as summarized into a receiver operating characteristic (ROC) plot, the area under the ROC curve (AUC) of 0.983 (95% CI: 0.983-0.984) was achieved.