ArticlePloS one2020
Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- A Deep Learning Approach for Tracking Colorectal Cancer-Derived Extracellular Vesicles in Colon and Lung Models.ACS biomaterials science & engineering · 2025Article
- IEGS-BoT: An Integrated Detection-Tracking Framework for Cellular Dynamics Analysis in Medical Imaging.Biomimetics (Basel, Switzerland) · 2025Article
- Enhanced stem cell image segmentation by leveraging visual processing mechanisms.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- Interdependency of estradiol-mediated ERα activation and subsequent PR and GREB1 induction to control cell cycle progression.Heliyon · 2024Article
- The multimodality cell segmentation challenge: toward universal solutions.Nature methods · 2024Article
- DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications.Expert systems with applications · 2024Article
- HeLa cell segmentation using digital image processing.Heliyon · 2024Article
- Defining the boundaries: challenges and advances in identifying cells in microscopy images.Current opinion in biotechnology · 2024Review
- Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage.IEEE open journal of engineering in medicine and biology · 2024Article
- A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.International journal of molecular sciences · 2023Article
- Facilitating cell segmentation with the projection-enhancement network.Physical biology · 2023Article
- The Cell Tracking Challenge: 10 years of objective benchmarking.Nature methods · 2023Article
- NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.Scientific reports · 2023Article
- Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Automated high-throughput image processing as part of the screening platform for personalized oncology.Scientific reports · 2023Article
- Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC).PloS one · 2023Article
- Synthesis of large scale 3D microscopic images of 3D cell cultures for training and benchmarking.PloS one · 2023Article
- BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.NPJ biofilms and microbiomes · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge (http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.