Analysis of microscopic images can be very challenging due to frequent interaction of cells with each other, low contrast, variations in cell shapes and similar cell appearances. Often a single frame does not contain enough information to make the correct decision about segmentation and tracking. In these situations it helps to consider content of adjacent frames, which can be computationally very expensive for long dense sequences. We have developed a greedy joint cell segmentation and tracking method which overcomes this challenge.
Cell Proposal Generation:
Our method uses multiple filter banks to detect cells, and uses watershed to split cell clusters and obtain cell proposals (Fig. 1 shows the main steps). It then create a hierarchical forest from these cell proposals (Fig. 2 shows a tree in the forest).
Our method connects proposals in adjacent frames and constructs a directed graphical model. This model contains nodes for different events that a cell can go through (mitosis, apoptosis, move, leave, enter). Our method then iteratively finds the shortest path in this graph, providing cell segmentations and tracks.
Fig. 1: Original Image, Binary segmented image, Filter responses for a filter bank, One set of cell proposals.
Fig. 2: A segmentation proposal tree. Left: Segmentations for 3 different filter banks. Right: Hierarchical tree constructed from the proposals.
S. U. Akram, J. Kannala, L. Eklund, and J. Heikkilä, (2016),
Joint Cell Segmentation And Tracking Using Cell Proposals, in IEEE International Symposium on Biomedical Imaging (ISBI)
Last updated: 26.8.2016