Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps

In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, which requires 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge and has a faster integer linear programming formulation. Moreover, our framework is flexible and supports segmentations from off-the-shelf cell segmentation models and can combine them into an ensemble that improves tracking. The code is available https://github.com/royerlab/ultrack.

Rethinking Interactive Image Segmentation: Feature Space Annotation

Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious -- a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space projection and optimized by metric learning as the labeling progresses. This strategy is in stark contrast to existing interactive segmentation methodologies, which perform annotation in the image domain. We show that our approach can surpass the accuracy of state-of-the-art methods in foreground segmentation datasets: iCoSeg, DAVIS, and Rooftop. Moreover, it achieves 91.5\% accuracy in a known semantic segmentation dataset, Cityscapes, being 74.75 times faster than the original annotation procedure. The appendix presents additional qualitative results. Code and video demonstration will be released upon publication.