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

• Published in: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) Vol. 2021; pp. 3354 - 3363
• Main Authors: Bao, Rina, Al-Shakarji, Noor M., Bunyak, Filiz, Palaniappan, Kannappan
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-10-2021
• Subjects: Image segmentation; Microscopy; Pipelines; Shape; Stem cells; Streaming media; Training
• ISSN: 2473-9944; 2473-9936; 2473-9944
• DOI: 10.1109/ICCVW54120.2021.00375

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/.