ArticleACS omega2021
ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices.
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- A Novel Single-Layer Microfluidic Device for Dynamic Stimulation, Culture, and Imaging of Mammalian Cells.Biosensors · 2025Article
- Improving engineered biological systems with electronics and microfluidics.Nature biotechnology · 2025Review
- Single-cell pathogen diagnostics for combating antibiotic resistance.Nature reviews. Methods primers · 2023Article
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.ACS synthetic biology · 2022Article
- microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.PloS one · 2022Article
- Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection.ACS omega · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.
Identifiers
What OpenQuestion holds
Registered trials
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.