Automatic Detection of Tumor Buds in Pan-Cytokeratin Stained Colorectal Cancer Sections by a Hybrid Image Analysis Approach - Bergler, Matthias; Benz, Michaela; Rauber, David; Hartmann, David; Kötter, Malte; Eckstein, Markus; Schneider-Stock, Regine; Hartmann, Arndt; Merkel, Susanne; Bruns, Volker; Wittenberg, Thomas; Geppert, Carol-Immanuel (Digital Pathology, 2019)
This contribution introduces a novel approach to the automatic detection of tumor buds in a digitalized pan-cytokeratin stained colorectal cancer slide. Tumor buds are representing an invasive pattern and are frequently investigated as a new diagnostic factor for measuring the aggressiveness of colorectal cancer. However, counting the number of buds under the microscope in a high power field by eyeballing is a strenuous, lengthy and error-prone task, whereas an automated solution could save time for the pathologists and enhance reproducibility. We propose a new hybrid method that consists of two steps. First possible tumor bud candidates are detected using a chain of classical image processing methods. Afterwards a convolutional deep neural network is applied to filter and reduce the number of false positive candidates detected in the first step. By comparing the automatically detected buds with a gold standard created by manual annotations, we gain a score of 0.977 for precision and 0.934 for sensitivity in our test sets on over 8.000 tumor buds.

Stitching Pathological Tissue Images using DOP Feature Tracking - Bergler, Matthias; Weiherer, Maximilian; Bergen, Tobias; Avenhaus, Malte; Rauber, David; Wittenberg, Thomas; Münzenmayer, Christian; Benz, Michaela (Bildverarbeitung für die Medizin 2018, 2018)
This contribution introduces an approach for stitching multiple images of a histological slide to a panorama image using Differences of Paraboloids (DOP). DOP provides a novel method for the detection, description and matching of features of two overlapping images. In our context of manual whole-slide imaging (WSI), DOP extracts essential keypoints of an image and describes them with feature vectors considering the keypoint's neighborhood. The DOP feature vector of the current image is then matched against the feature vectors of all previous images. With matching correspondences, a feature based image registration is generated that estimates the translation between two overlapping images. Likewise, all images are aligned to form a whole-slide panorama. Our results reveal a superior stitching quality employing the presented DOP approach in comparison to the well-known SIFT and SURF. Our evaluation is based on the homogeneity at the artifically created edges in the panorama due to the stitching. The DOP offers a convincing solution to stitch pathological tissue.

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms - Wiesmann, Veit; Bergler, Matthias; Franz, Daniela; Palmisano, Ralf; Prinzen, Martin; Wittenberg, Thomas (BMC Bioinformatics, 2017)
Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines.