Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Voting Accelerated by Graphics Processing Units (GPU)
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Inferring Segmented Dense Motion Layers Using 5D Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and tracking algorithms for monitoring cellular motion and function
Segmentation and tracking algorithms for monitoring cellular motion and function
Some assignment problems arising from multiple target tracking
Mathematical and Computer Modelling: An International Journal
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Random walks in directed hypergraphs and application to semi-supervised image segmentation
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Living immune system microenvironments can be imaged by timelapse multi-photon multi-spectral microscopy to reveal the complex tissue architecture and cell movements. Automated segmentation and tracking of these motile, numerous, and densely packed cells over long-duration 3-D movies is needed to sense and quantify subtle phenotypic differences between genetically modified and wild type cells. We present a novel multi-temporal 3-D cell tracking algorithm that: (i) implicitly models and corrects segmentation errors by exploiting spatio-temporal continuity, (ii) computes globally-optimal second-order correspondences through second-order matching in a directed hypergraph, (iii) does not require any manual initialization, and (iv) utilizes a trainable nonparametric motion model using smooth kernel density estimation. The tracking problem is formulated as a second-order hyperedge selection problem in a directed hypergraph, and solved using branch-and-cut integer programming. A quantitative study on four real datasets containing 3,361 cells showed that our algorithm reduces segmentation errors by 53% post-tracking compared to independent segmentations in every frame. In comparison, Jaqaman et al.'s u-track [1] algorithm eliminates only 38% of the segmentation errors. We found the error rate of our tracking algorithm to be 2.23%, 28.8% lesser than u-track's error rate while comparing 7,213 track correspondences.