F. Bunyak, and
IEEE International Conference on Computer Vision Workshops (ICCVW),
Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research. A continuing challenge is the segmentation of dense touching cells and deforming cells with indistinct boundaries, in low signal-to-noise-ratio images. In this paper, we present a dual-stream marker-guided network (DMNet) for segmentation of touching cells in microscopy videos of many cell types. DMNet uses an explicit cell marker-detection stream, with a separate mask-prediction stream using a distance map penalty function, which enables supervised training to focus attention on touching and nearby cells. For multi-object cell tracking we use M2Track tracking-by-detection approach with multi-step data association. Our M2Track with mask overlap includes short term track-to-cell association followed by track-to-track association to re-link tracklets with missing segmentation masks over a short sequence of frames. Our combined detection, segmentation and tracking algorithm has proven its potential on the IEEE ISBI 2021 6th Cell Tracking Challenge (CTC-6) where we achieved multiple top three rankings for diverse cell types. Our team name is MU-Ba-US, and the implementation of DMNet is available at, http://celltrackingchallenge.net/participants/MU-Ba-US/.
author = "R. Bao and N. Al-Shakarji and F. Bunyak and K. Palaniappan",
title = "DMNet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking",
year = 2021,
booktitle = "IEEE International Conference on Computer Vision Workshops (ICCVW)",
pages = "3354-3363",
keywords = "training, image segmentation, shape, microscopy, stem cells, cell detection, cell tracking, mitosis",
doi = "10.1109/ICCVW54120.2021.00375",
url = "https://ieeexplore.ieee.org/document/9607707"
R. Bao, N. Al-Shakarji, F. Bunyak, and K. Palaniappan. DMNet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking. IEEE International Conference on Computer Vision Workshops (ICCVW), pages 3354-3363, 2021.