# ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

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## Abstract

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## Featured Application

**Novel method for Automated Cell Detection and Counting (ACDC) in time-lapse fluorescence microscopy.**

## Abstract

## 1. Introduction

- ACDC is designed and developed to cope with the analysis of stacks of time-lapse microscopy images in real-time;
- ACDC does not require any training phase, and represents a reliable solution even without the availability of large-scale annotated datasets.

## 2. Materials and Methods

#### 2.1. Fluorescence Microscopy Imaging Data

#### 2.1.1. Vanderbilt University Dataset

#### 2.1.2. 2018 Data Science Bowl

#### 2.2. Acdc: A Method for the Automatic Cell Detection and Counting

#### 2.2.1. Pre-Processing

- Application of bilateral filtering that allows for denoising the image $\mathcal{I}$ while preserving the edges by means of a non-linear combination of nearby image values [38]. This noise-reducing smoothing filter combines gray levels (colors) according to both a geometric closeness function c and a radiometric (photometric) similarity function s. This combination is used to strengthen near values with respect to distant values in both spatial and intensity domains. This simple yet effective strategy allows for contrast enhancement [49]. Bilateral filter has been shown to work properly in fluorescence imaging even preserving the directional information, such as in the case of the F-actin filaments [50]. This denoising technique was effectively applied to biological electron microscopy [51], as well as to cell detection [52], revealing better performance—compared to low-pass filtering—in noise reduction without removing the structural features conveyed by strong edges. The most commonly used version of bilateral filtering is the shift-invariant Gaussian filtering, wherein both the closeness function c and the similarity function s are Gaussian functions of the Euclidean distance between their arguments [38]. With more details, c is radially symmetric: $c(\mathbf{p},\mathbf{q})={e}^{-\frac{1}{2}{\left(\right)}^{\frac{\left|\right|\mathbf{p}-\mathbf{q}\left|\right|}{{\sigma}_{s}}}2}$. Consistently, the similarity function s can be defined as: $s(\mathbf{p},\mathbf{q})={e}^{-\frac{1}{2}{\left(\right)}^{\frac{\left|\right|I\left(\mathbf{p}\right)-I\left(\mathbf{q}\right||}{{\sigma}_{c}}}2}$. In ACDC we set ${\sigma}_{c}=1$ and ${\sigma}_{s}={\sigma}_{\mathrm{global}}$ (where ${\sigma}_{\mathrm{global}}$ is the the standard deviation of the input image $\mathcal{I}$) for the standard deviation of the Gaussian functions c and s, respectively. This smart denoising approach allows us to keep the edge sharpness while reducing the noise of the processed image, so avoiding cell region under-estimation.
- Application of top-hat transform for background correction with a binary circular structuring element (radius: 21 pixels) on the smoothed image. This operation accounts for non-uniform illumination artifacts, by extracting the nuclei from the background. The white top-hat transform is the difference between the input image I and the opening of $\mathcal{I}$ with a gray-scale structuring element b: ${\mathcal{T}}_{\mathrm{w}}=\mathcal{I}-\mathcal{I}\circ b$ [53].

#### 2.2.2. Nucleus Seed Selection

- A thresholding technique has to be first applied to detect the cell regions. Both global and local thresholding techniques aim at separating foreground objects of interest from the background in an image, considering differences in pixel intensities [54]. Global thresholding determines a single threshold for all pixels and works well if the histogram of the input image contains well-separated peaks corresponding to the desired foreground objects and background [55]. Local adaptive thresholding techniques estimate the threshold locally over sub-regions of the entire image, by considering only a user-defined window with a specific size and exploiting local image properties to calculate a variable threshold [53,54]. These algorithms find the threshold by locally examining the intensity values of the neighborhood of each pixel according to image intensity statistics. To avoid unwanted pixels in the thresholded image, mainly due to small noisy hyper-intense regions caused by non-uniform illumination, we apply the Otsu global thresholding method [55] instead of local adaptive thresholding based on the mean value in a neighborhood [56]. Moreover, global threshold techniques are significantly faster than local adaptive strategies.
- Hole filling is applied to remove possible holes in the detected nuclei due to small hypo-intense regions included in the nuclei regions.
- Morphological opening (using a disk with 1-pixel radius as a structuring element) is used to remove loosely connected-components, such as in the case of almost overlapping cells.
- Unwanted areas are removed according to the connected-components size. In particular, the detected candidate regions with areas smaller than 40 pixels are removed to refine the achieved segmentation results by robustly avoiding false positives.
- Morphological closing (using a 2-pixel radius circular structuring element) is applied to smooth the boundaries of the detected nuclei and avoid the under-estimation of the detected nuclei regions.
- The approximate Euclidean distance transform (EDT) from the binary mask, achieved by applying the Otsu algorithm and refined by using the previous 3 steps, is used to obtain the matrix of distances of each pixel to the background by exploiting the ${\ell}_{2}$ Euclidean distance [57] (with a $5\times 5$ pixel mask for a more accurate distance estimation). This algorithm calculates the distance to the closest background pixel for each pixel of the source image. Let $\mathcal{G}$ be a regular grid and $f:\mathcal{G}\to \mathbb{R}$ an arbitrary function on the grid, called a sampled function [58]. We define the distance transform ${\mathcal{D}}_{f}:\mathcal{G}\to \mathbb{R}$ of f as:$${\mathcal{D}}_{f}\left(\mathbf{p}\right)=\underset{\mathbf{q}\in \mathcal{G}}{min}\left(\right)open="("\; close=")">d(\mathbf{p},\mathbf{q})+f\left(\mathbf{q}\right)$$$${\mathcal{D}}_{\mathcal{P}}=\underset{\mathbf{q}\in \mathcal{P}}{min}\left(\right)open="("\; close=")">d(\mathbf{p},\mathbf{q})+\mathrm{\U0001d7d9}\left(\mathbf{q}\right)$$$$\mathrm{\U0001d7d9}\left(\mathbf{q}\right)=\left(\right)open="\{"\; close>\begin{array}{cc}0,\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}\mathbf{q}\in \mathcal{P}\hfill \\ \infty ,\hfill & \mathrm{otherwise}\hfill \end{array}$$
- Regional maxima computation allows for estimating foreground peaks on the normalized distance map. Regional maxima are connected-components of pixels with a constant intensity value, whose external boundary pixels have all a lower intensity value [42]. The resulting binary mask contains pixels that are set to 1 for identifying regional maxima, while all other pixels are set to 0. A $5\times 5$ pixel square was employed as structuring element.
- Morphological dilation (using a 3-pixel radius circular structuring element) is applied to the foreground peaks previously detected for better defining the foreground regions and merging neighboring local minima into a single seed point. The segmentation results on Figure 5a,b are shown in Figure 6a,b, respectively. The detail in Figure 5a shows that ACDC is highly specific to cell nuclei detection, discarding non-cell regions related to acquisition artifacts.

#### 2.2.3. Cell Nuclei Segmentation Using the Watershed Transform

#### 2.2.4. Implementation Details

`mpi4py`, which provides bindings of the Message Passing Interface (MPI) specifications for Python to leverage multi-core and many-core resources [69]. The distributed strategy used to accelerate ACDC is similar to that employed in [70,71,72], where the Parent allocates the resources and orchestrates the workers, which run ACDC to analyze the assigned images. This distributed version of ACDC is $3.7\times $ faster than the sequential version by exploiting 6 cores of a CPU Intel Core E5-2650 v4 (clock $2.2$ GHz).

#### 2.3. Segmentation Evaluation Metrics

## 3. Results

#### 3.1. ACDC Performance

#### 3.2. Comparison with Other Cell Imaging Tools and Segmentation Methods

## 4. Discussion and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**,

**b**) Examples of the analyzed microscopy fluorescence images provided by the Department of Biochemistry of the VU. The images were displayed by automatically adjusting the brightness and contrast according to an histogram-based procedure.

**Figure 3.**Boxplots depicting the distribution for both the analyzed datasets in terms of: (

**a**) total number of cells, and (

**b**) coverage of the cell nuclei regions.

**Figure 4.**Flow diagram of the ACDC pipeline. The gray, black and light-blue data blocks denote gray-scale images, binary masks and information extracted from the images, respectively. The three macro-blocks represent the three main processing phases, namely: pre-processing, seed selection, and watershed-based segmentation.

**Figure 8.**Comparison of the gold standard cell nuclei segmentation (magenta contour in the left images) against the automated result obtained by ACDC (segmented nuclei over-imposed onto the original fluorescence images with alpha-blending in the right images): (

**a**) VU dataset, where the orange arrows denote errors in the split of clustered cell nuclei; (

**b**) DSB dataset, where the green arrows denote groups of cells that were erroneously delineated as unique connected-components in the gold standard and the blue dashed boxes represent spurious speckles that are not detected by ACDC.

**Figure 9.**Scatter plots depicting ACDC results compared to the gold standard in terms of cell nuclei counting in the case of: (

**a**) time-lapse fluorescence images from the VU dataset; (

**b**) small fluorescent nuclei images from the DSB dataset. The equality line through the origin is drawn as a dashed line.

**Figure 10.**Bland-Altman plots of the cell counting measurements achieved by ACDC versus the gold standard for the (

**a**) VU and (

**b**) DSB datasets. Solid horizontal and dashed lines denote the mean and $\pm 1.96$ standard deviation values, respectively.

**Figure 11.**Regplots showing the scatter plots obtained by considering the number of manually detected cells (y-axis) and the number of cells automatically detected (x-axis) with ACDC (left), ImageJ with Gaussian filter (center), and ImageJ without Gaussian filter (right), along with the fitted regression model (regression line and the $95\%$ confidence interval for that regression). Plots (

**a**–

**c**) report the results obtained on the VU dataset, while plots (

**d**–

**f**) are obtained from the DSB dataset.

**Figure 12.**Examples of cell nuclei segmented by using ACDC and the implemented ImageJ pipelines with and without Gaussian filtering. (

**a**,

**b**) present images taken from the VU dataset, with different visual characteristics.

**Table 1.**Evaluation metrics on cell counting and segmentation achieved by ACDC (with and without bilateral filtering) on the analyzed time-lapse microscopy VU and 2018 DSB datasets, comprising 46 and 301 images, respectively. The results for the DSC and IoU metrics, as well as the execution time measurements, are expressed as mean value ± standard deviation.

Method | Dataset | Pearson Coeff. (p-Value) | DSC (%) | IoU (%) | Exec. Time (s) |
---|---|---|---|---|---|

ACDC (without bilateral filter) | VU | $\rho =0.99$$(p=2.5\times {10}^{-74})$ | $75.86\pm 5.98$ | $61.45\pm 7.47$ | $3.98\pm 0.10$ |

ACDC (with bilateral filter) | VU | $\rho =0.99$$(p=6.6\times {10}^{-74})$ | $76.84\pm 6.71$ | $62.84\pm 8.58$ | $7.49\pm 0.30$ |

ACDC (without bilateral filter) | DSB | $\rho =0.96$$(p=1.1\times {10}^{-175})$ | $87.34\pm 6.89$ | $77.97\pm 9.49$ | $0.07\pm 0.05$ |

ACDC (with bilateral filter) | DSB | $\rho =0.96$$(p=2.6\times {10}^{-169})$ | $88.64\pm 7.41$ | $80.37\pm 10.58$ | $0.12\pm 0.09$ |

**Table 2.**Comparison of ACDC and the tested ImageJ pipelines in terms of cell nuclei counting and segmentation. The results are expressed as mean value ± standard deviation.

Method | Dataset | Pearson Coeff. (p-Value) | DSC (%) | IoU (%) |
---|---|---|---|---|

ACDC | VU | $\rho =0.99$$(p=6.6\times {10}^{-74})$ | $76.84\pm 6.71$ | $62.84\pm 8.58$ |

ImageJ (with Gaussian filter) | VU | $\rho =0.99$$(p=9.5\times {10}^{-75})$ | $74.97\pm 6.17$ | $60.32\pm 7.62$ |

ImageJ (without Gaussian filter) | VU | $\rho =0.99$$(p=5.3\times {10}^{-44})$ | $74.43\pm 6.20$ | $59.62\pm 7.61$ |

ACDC | DSB | $\rho =0.96$$(p=2.6\times {10}^{-169})$ | $88.64\pm 7.41$ | $80.37\pm 10.58$ |

ImageJ (with Gaussian filter) | DSB | $\rho =0.97$$(p=5.7\times {10}^{-205})$ | $86.50\pm 6.86$ | $76.78\pm 9.66$ |

ImageJ (without Gaussian filter) | DSB | $\rho =0.97$$(p=3.4\times {10}^{-207})$ | $86.68\pm 7.75$ | $77.23\pm 10.86$ |

**Table 3.**Comparison of ACDC, CellProfiler, MC-Watershed, and SM-Watershed in terms of cell nuclei segmentation on the SNPHEp-2 dataset.

Method | DSC (%) | IoU (%) |
---|---|---|

ACDC | $80.92$ | $67.96$ |

CellProfiler | $78.12$ | $64.10$ |

MC-Watershed | $86.54$ | $76.27$ |

SM-Watershed | $83.10$ | $71.09$ |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Rundo, L.; Tangherloni, A.; Tyson, D.R.; Betta, R.; Militello, C.; Spolaor, S.; Nobile, M.S.; Besozzi, D.; Lubbock, A.L.R.; Quaranta, V.;
et al. ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy. *Appl. Sci.* **2020**, *10*, 6187.
https://doi.org/10.3390/app10186187

**AMA Style**

Rundo L, Tangherloni A, Tyson DR, Betta R, Militello C, Spolaor S, Nobile MS, Besozzi D, Lubbock ALR, Quaranta V,
et al. ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy. *Applied Sciences*. 2020; 10(18):6187.
https://doi.org/10.3390/app10186187

**Chicago/Turabian Style**

Rundo, Leonardo, Andrea Tangherloni, Darren R. Tyson, Riccardo Betta, Carmelo Militello, Simone Spolaor, Marco S. Nobile, Daniela Besozzi, Alexander L. R. Lubbock, Vito Quaranta,
and et al. 2020. "ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy" *Applied Sciences* 10, no. 18: 6187.
https://doi.org/10.3390/app10186187