DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking

Rina Bao, Noor M. Al-Shakarji, Filiz Bunyak, Kannappan Palaniappan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 3361-3370

Abstract


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 trajectory-to-cell association followed by trajectory-to-trajectory 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 in 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/.

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[bibtex]
@InProceedings{Bao_2021_ICCV, author = {Bao, Rina and Al-Shakarji, Noor M. and Bunyak, Filiz and Palaniappan, Kannappan}, title = {DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {3361-3370} }