YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy

Salem, Danny; Li, Yifeng; Xi, Pengcheng; Phenix, Hilary; Čuperlović-Culf, Miroslava; Kærn, Mads

doi:10.3390/app11062692

Cited by 16 publications

(14 citation statements)

References 48 publications

(65 reference statements)

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“…The automation of hardware and software for microscopy has resulted in researchers' ability to generate massive datasets containing images of cells over time. For example, in a recent high throughput experiment Bakshi et al imaged 8 Escherichia coli over days by acquiring 705 field of views every few minutes (1). Additionally, recent studies have used closed-loop microscopy and optogenetic platforms to control gene expression in single cells in real time (2)(3)(4).…”

Section: Introductionmentioning

confidence: 99%

“…U-Net uses a "U"-shaped network architecture with a contraction path, where successive convolutional layers are progressively down-sampled, followed by a symmetric expansion path where the low-resolution but high-level encoding of the input image is up-sampled back to the original resolution. This approach has been widely successful for segmentation of cells (6)(7)(8)(9)(10) and for tracking cells from frame-to-frame within time-lapse images (7,9).…”

Section: Introductionmentioning

confidence: 99%

“…Software based on traditional image analysis techniques such as Schnitzcells (17), Oufti (18), or SuperSegger (19) all require significant user input and post-processing. A few recent studies have proposed deep learning models for bacterial or yeast cell segmentation (6,8,10,20), however to our knowledge there is no integrated segmentation and tracking pipeline for two-dimensional timelapse analysis of bacteria.…”

Section: Introductionmentioning

confidence: 99%

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DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Alnahhas

Lugagne

Dunlop

2021

Preprint

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Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Alnahhas

Lugagne

Dunlop

2021

Preprint

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“…It has been demonstrated that the U-Net architecture and its variants such as Unet++ (Zhou et al, 2018 ), 3D Unet (Çiçek et al, 2016 ), and V-Net (Milletari et al, 2016 ) can obtain high segmentation accuracy. Motivated by the good performance of U-Nets in cell segmentation (Van Valen et al, 2016 ; Hollandi et al, 2020 ; Salem et al, 2020 ), we developed Dice-XMBD, a deep neural network (DNN)-based cell segmentation method for multichannel IMC images. Dice-XMBD is marker agnostic and can perform cell segmentation for IMC images of different channel configurations without modification.…”

Section: Introductionmentioning

confidence: 99%

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

Qiao

Jiao

et al. 2021

Front. Genet.

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Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

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“…It has been demonstrated that the U-Net architecture and its variants such as Unet++[34], 3D Unet [35] and V-Net [36] can obtain high segmentation accuracy. Motivated by the outstanding performance of using U-Nets for cell segmentation [37, 38, 39], we developed Dice-XMBD, a deep neural network (DNN)-based cell segmentation method for multichannel IMC images. Dice-XMBD is marker agnostic and can perform cell segmentation for IMC images of different channel configurations without modification.…”

Section: Introductionmentioning

confidence: 99%

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

Qiao

Jiao

et al. 2021

Preprint

View full text Add to dashboard Cite

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

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YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy

Cited by 16 publications

References 48 publications

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

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