Identification of individual cells from z-stacks of bright-field microscopy images

Lugagne, Jean-Baptiste; Jain, Srajan; Ivanovitch, Pierre; Meriem, Zacchari Ben; Vulin, Clément; Fracassi, Chiara; Batt, Grégory; Hersen, Pascal

doi:10.1038/s41598-018-29647-5

Cited by 22 publications

(22 citation statements)

References 15 publications

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“…MAARs 33 and the MPCS algorithm 32 use correlation analysis. These methods have been further complemented by machine learning 35 , 62 or neural networks 36 . Other methods do not use the pattern itself, but use traditional segmentation on brightfield images: either edge detection 63 or adaptive thresholding 34 .…”

Section: Discussionmentioning

confidence: 99%

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

Baybay

Esposito

Hauf

2020

Sci Rep

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Three-dimensional (3D) segmentation of cells in microscopy images is crucial to accurately capture signals that extend across optical sections. Using brightfield images for segmentation has the advantage of being minimally phototoxic and leaving all other channels available for signals of interest. However, brightfield images only readily provide information for two-dimensional (2D) segmentation. In radially symmetric cells, such as fission yeast and many bacteria, this 2D segmentation can be computationally extruded into the third dimension. However, current methods typically make the simplifying assumption that cells are straight rods. Here, we report Pomegranate, a pipeline that performs the extrusion into 3D using spheres placed along the topological skeletons of the 2D-segmented regions. The diameter of these spheres adapts to the cell diameter at each position. Thus, Pomegranate accurately represents radially symmetric cells in 3D even if cell diameter varies and regardless of whether a cell is straight, bent or curved. We have tested Pomegranate on fission yeast and demonstrate its ability to 3D segment wild-type cells as well as classical size and shape mutants. The pipeline is available as a macro for the open-source image analysis software Fiji/ImageJ. 2D segmentations created within or outside Pomegranate can serve as input, thus making this a valuable extension to the image analysis portfolio already available for fission yeast and other radially symmetric cell types.

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Section: Discussionmentioning

confidence: 99%

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

Baybay

Esposito

Hauf

2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…At the beginning of this project, we explored the alternative of segmenting and labeling each cell individually before classification, but the extremely irregular cellular contours and the occasional overlap among them made this approach inapplicable. We believe the work of Lugagne et al 31 highlights the next steps to overcome these issues. The curated image data was of paramount importance for the achieved performances of the classifiers.…”

Section: Discussionmentioning

confidence: 96%

Learning Deep Features for Stain-free Live-dead Human Breast Cancer Cell Classification

Pattarone¹,

Ación²,

Simian³

et al. 2020

Preprint

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Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. To our knowledge, there is no previous work attempting this task on in vitro studies of breast cancer cells, nor is there a dataset available to explore solutions related to this issue. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several state-of-the-art classifiers were trained based on convolutional neural networks (CNN) to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.

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“…MAARs 33 and the MPCS algorithm 32 use correlation analysis. These methods have been further complemented by machine learning 35,62 or neural networks 36 . Other methods do not use the pattern itself, but use traditional segmentation on brightfield images: either edge detection 63 or adaptive thresholding 34 .…”

Section: Discussionmentioning

confidence: 99%

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

Baybay

Esposito

Hauf

2020

Preprint

View full text Add to dashboard Cite

AbstractThree-dimensional (3D) segmentation of cells in microscopy images is crucial to accurately capture signals that extend across optical sections. Using brightfield images for segmentation has the advantage of being minimally phototoxic and leaving all other channels available for signals of interest. However, brightfield images readily only provide two-dimensional (2D) segmentation. In radially symmetric cells, such as fission yeast and many bacteria, the brightfield 2D segmentation can be computationally extruded into the third dimension. However, current methods make simplifying assumptions that introduce error. Here, we report Pomegranate, a pipeline that performs the extrusion using spheres placed along the topological skeletons of the 2D-segmented regions. The diameter of these spheres adapts to the cell diameter at each position. Thus, Pomegranate accurately represents cells in 3D even if cell diameter varies and regardless of whether a cell is straight, bent or curved. We have tested Pomegranate on fission yeast and demonstrate its ability to 3D segment wild-type cells as well as classical size and shape mutants. The pipeline is available as macro for the open-source image analysis software Fiji/ImageJ. 2D segmentations created within or outside Pomegranate can serve as input, thus making this a valuable extension to the image analysis portfolio already available for fission yeast and other rod-shaped cell types.

show abstract

Identification of individual cells from z-stacks of bright-field microscopy images

Cited by 22 publications

References 15 publications

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

Learning Deep Features for Stain-free Live-dead Human Breast Cancer Cell Classification

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

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