Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
, Left, average errors per ground truth edge for each error type; middle top, fraction of error-free tracks for a given track length; middle bottom, top right, and bottom right, example ground truth track with superimposed tracking result for our method , the greedy baseline , and TGMM , respectively. Other than the dashed false positive divisions, we only show detections that matched the selected ground truth track.
The ability to densely reconstruct cell lineages in such large, information-rich datasets opens up vast opportunities for exploring cell fate dynamics and tissue morphogenesis. Accurately following cells and their progeny over extended time periods allows identification of individual cell behaviors that are not visible with shorter and less accurate lineages.
The cell indicator network is trained on sparse point annotations and predicts the centers of cell nuclei. The training signal for this network, called the cell indicator value, is a Gaussian with maximum value 1 at the cell center annotation and decreasing according to a hyperparameter. With only sparse annotations, it is unknown if pixels far from cell center annotations are background or cells that were not annotated.
In addition to the cell indicator network, we train a movement vector network to predict the movement of cells between frames. For a pixel near to a cell in frame, the movement vector is a 3D offset vector that points to the relative location of the center of the same cell in frame− 1. Predicting the offset to the same cell in the previous time frame, rather than the next time frame, allows divisions to be represented naturally, since each daughter cell points to the center of the parent cell.
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