Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE
This work addresses cell analysis in biomedical imaging, but it is incremental as it combines existing methods like CNNs and watershed algorithms.
The paper tackled cell segmentation and tracking in microscopy images by developing a 3-step approach involving detection, tracking, and segmentation, achieving results for the ISBI 2019 benchmark competition.
The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei. In the preprocessing phase, we detect centroids of the cell nuclei using a convolutional neural network (CNN) for the 2D images and a Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data sets. Tracking was performed in a backwards fashion on the predicted seed points, i.e., starting at the last frame and sequentially connecting corresponding objects until the first frame was reached. Correspondences were identified by propagating detections of a frame t to its preceding frame t-1 and by combining redundant detections using a hierarchical clustering approach. The tracked centroids were then used as input to variants of the seeded watershed algorithm to obtain the final segmentation.