Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning

Theriault, Diane; Walker, Matthew; Wong, Joyce Y.; Betke, Margrit

doi:10.1007/s00138-011-0345-9

Cited by 31 publications

(29 citation statements)

References 26 publications

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“…6,11,38 The overall precision, recall, and F-score were 0.859 AE 0.022, 0.815 AE 0.058, and 0.836 AE 0.039, respectively. The classification using both sets of features led to higher performance of the classifier, with the overall accuracy of the classification reaching 0.956 AE 0.011.…”

Section: Resultsmentioning

confidence: 97%

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Automated classification of cell morphology by coherence-controlled holographic microscopy

et al. 2017

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Abstract. In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherencecontrolled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

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Section: Resultsmentioning

confidence: 97%

“…Another study 11 presents application of machine learning techniques to analysis of cell morphology in phase-contrast microscopy images. However, the images gained by phasecontrast microscopy demonstrate halo artifacts, which makes the boundaries of the cells appear brighter and makes the segmentation challenging and inaccurate.…”

Section: Introductionmentioning

confidence: 99%

Automated classification of cell morphology by coherence-controlled holographic microscopy

et al. 2017

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“…The vertical column on the left represents the image size, and the horizontal column above represents the size of the kernel. For example, [3][4][5][6] indicates that the kernel size of the first convolution layer is 3 × 3 and the kernel size of the second convolution layer is 6 × 6. As a combination of kernel sizes, a total of nine ways are implemented.…”

Section: Phase-contrast and Fluorescent Images Of Cultured C2c12 Cellsmentioning

confidence: 99%

“…For example, cellular morphology classification [5], prediction of osteogenic differentiation potential [6], cellular orientation analysis [7], and non-invasive evaluation of human induced pluripotent stem cell (iPSC) [8] have been reported. Because cells have complicated and ununiformed morphology, extracting of feature parameters to obtain highly accurate classifications are still developing field.…”

Section: Introductionmentioning

confidence: 99%

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

et al. 2017

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In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

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“…During the preprocessing step, we followed the work by Theriault et al [8] to obtain the initial segmentation. The Hungarian algorithm is adopted to solve the frame-by-frame data association problem and identify merging and splitting events.…”

Section: Methodsmentioning

confidence: 99%

Hierarchical Partial Matching and Segmentation of Interacting Cells

Gurari

Wong

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. We propose a method that automatically tracks and segments living cells in phase-contrast image sequences, especially for cells that deform and interact with each other or clutter. We formulate the problem as a many-to-one elastic partial matching problem between closed curves. We introduce Double Cyclic Dynamic Time Warping for the scenario where a collision event yields a single boundary that encloses multiple touching cells and that needs to be cut into separate cell boundaries. The resulting individual boundaries may consist of segments to be connected to produce closed curves that match well with the individual cell boundaries before the collision event. We show how to convert this partial-curve matching problem into a shortest path problem that we then solve efficiently by reusing the computed shortest path tree. We also use our shortest path algorithm to fill the gaps between the segments of the target curves. Quantitative results demonstrate the benefit of our method by showing maintained accurate recognition of individual cell boundaries across 8068 images containing multiple cell interactions.

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Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning

Cited by 31 publications

References 26 publications

Automated classification of cell morphology by coherence-controlled holographic microscopy

Automated classification of cell morphology by coherence-controlled holographic microscopy

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

Hierarchical Partial Matching and Segmentation of Interacting Cells

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