Cellpose: a generalist algorithm for cellular segmentation

Stringer, Carsen; Wang, Yichen; Michaelos, Michalis; Pachitariu, Marius

doi:10.1038/s41592-020-01018-x

Cited by 1,947 publications

(1,834 citation statements)

References 44 publications

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“…The cell movement assumption is motivated by the need of a reasonable temporal resolution of the image sequence for a detailed analysis of cell lineage or cell behavior. The segmentation assumption is motivated by the availability of reasonably well-performing segmentation approaches [7][8][9][10][11]. Due to potentially occurring segmentation errors, we refer to segmentation masks as segmented objects and not as cells, as the segmentation masks can contain an as cell detected artifact, only parts of a cell, a single cell or several cells.…”

Section: Methodsmentioning

confidence: 99%

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Löffler

Scherr

Mikut

2021

Preprint

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Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We investigate the performance of our approach by simulating erroneous segmentation data, including false negatives, over- and under-segmentation errors, on 2D and 3D cell data sets. We compare our approach against three well-performing tracking algorithms from the Cell Tracking Challenge. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. Furthermore, in case of under-segmentation or a combination of segmentation errors our approach outperforms the other tracking approaches.

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Section: Methodsmentioning

confidence: 99%

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Löffler

Scherr

Mikut

2021

Preprint

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“…Subsequent applications of ImageNet make clear that machine learning models are only as strong as the underlying training and validation data. In biomedical research, segmentation methods for cell lines (Caicedo et al, 2019;Liu et al, 2020;Torr et al, 2020) have benefited from large public datasets such as Expert Visual Cell ANnotation (EVICAN) (Schwendy, Unger and Parekh, 2020), Cellpose (Stringer et al, 2020), the Broad Bioimage Benchmark Collection (BBBC) (Ljosa, Sokolnicki and Carpenter, 2012), The Cancer Genome Atlas (TCGA) (Xing et al, 2019), and Kaggle datasets (Yang et al, 2020).…”

Section: Biomedical Image Datasetsmentioning

confidence: 99%

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Yapp

Novikov

Jang

et al. 2021

Preprint

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Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation. This starts with identification of nuclei, a challenging problem in tissue imaging that has recently benefited from deep learning. In this paper we demonstrate two generally applicable approaches to improving segmentation accuracy as scored using new human-labelled segmentation masks spanning multiple human tissues. The first approach involves the use of real augmentations during training. These comprise defocused and saturated image data and improve model accuracy when computational augmentation (Gaussian blurring) does not. The second involves collection of nuclear envelope data. The two approaches cumulatively and substantially improve segmentation with three different deep learning frameworks, yielding a set of high accuracy segmentation models. Moreover, the use of real augmentation may have applications outside of microscopy.

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“…Even just a few years ago, advanced programming skills were needed to implement data science pipelines. Recently, however, user interfaces and other tools have been developed (Bannon et al, 2021;Fazeli et al, 2020;Ouyang et al, 2019a;Stringer et al, 2021;Von Chamier et al, 2020 preprint), rendering data science in cell imaging more accessible to a wide range of researchers.…”

Section: Data Science In Cell Biologymentioning

confidence: 99%

“…2020; Stringer et al, 2021;Van Valen et al, 2016) and tracking (Ulman et al, 2017). Indeed, most efforts in the thriving bioimage informatics community have been invested in these types of automation and tool building projects (Meijering et al, 2016).…”

Section: Moving Beyond Tool Buildingmentioning

confidence: 99%

Data science in cell imaging

Driscoll

Zaritsky

2021

Journal of Cell Science

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Cell imaging has entered the ‘Big Data’ era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the ‘omics’ fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools – democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

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Cellpose: a generalist algorithm for cellular segmentation

Cited by 1,947 publications

References 44 publications

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Data science in cell imaging

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