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

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

doi:10.1101/2020.11.30.402917

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…Among the different imaging modalities, brightfield illumination with transmitted light is the simplest to acquire while avoiding damaging the sample 1 . The usefulness of this technology has led to its widespread adoption 2–4 , and thereby to a dramatic increase in the volumes of microscopy data. However, the automated analysis techniques required to extract information at scale are often hindered by the artifacts present in the images 5,6 .…”

Section: Introductionmentioning

confidence: 99%

ArtSeg: Rapid Artifact Segmentation and Removal in Brightfield Cell Microscopy Images

Ali

Hollo

Laasfeld

et al. 2022

Preprint

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Brightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by orders of magnitude compared to pixel-level annotation, but without substantially sacrificing the segmentation performance. We confirm that artifacts indeed exist with different shapes and sizes in three different brightfield microscopy image datasets, and distort downstream analyses such as nuclei segmentation, morphometry and fluorescence intensity quantification. We then demonstrate that our automated artifact removal ameliorates this problem. Such rapid cleaning of acquired images using the power of deep learning models is likely to become a standard step for all large scale microscopy experiments.

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Section: Introductionmentioning

confidence: 99%

ArtSeg: Rapid Artifact Segmentation and Removal in Brightfield Cell Microscopy Images

Ali

Hollo

Laasfeld

et al. 2022

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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“…Although many software tools dedicated to the analysis of budding yeast live-cell microscopy have been developed in the past (ImageJ/Fiji 10 , MorphoLibJ 11 , PhyloCell 12 , CellProfiler 13 , Cell Tracer 14 , Wood et al 15,16 , Cell Star 17 , Cell Serpent 18 , CellID 19 , Tracker 20 , DISCO 21 , YeastSpotter 22 , YeastNet 23 , DeepCell 24 , Cellbow 25 ), to the best of our knowledge, none of them spanned the entire image analysis pipeline from CNN-base segmentation to cell cycle analysis, and fluorescent signal quantification.…”

Section: Introductionmentioning

confidence: 99%

Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

Padovani

Mairhoermann

Falter‐Braun

et al. 2021

Preprint

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Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC

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

Cited by 8 publications

References 27 publications

ArtSeg: Rapid Artifact Segmentation and Removal in Brightfield Cell Microscopy Images

ArtSeg: Rapid Artifact Segmentation and Removal in Brightfield Cell Microscopy Images

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

Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

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