Improving reliability of live/dead cell counting through automated image mosaicing

Piccinini, Filippo; Tesei, Anna; Paganelli, Giulia; Zoli, Wainer; Bevilacqua, Alessandro

doi:10.1016/j.cmpb.2014.09.004

Cited by 17 publications

(17 citation statements)

References 80 publications

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“…The need for a gold standard that quantifies human variability is well‐known and well‐studied in other fields, such as automatic image segmentation, cell counting, or in machine learning (Boccardi et al, ; Entis, Doerga, Barrett, & Dickerson, ; Kleesiek et al, ; Piccinini, Tesei, Paganelli, Zoli, & Bevilacqua, ). For applications such as hippocampi or tumor segmentation, thorough assessments of reproducibility and multiple iterations of manual segmentation protocols already exist (Boccardi et al, ; Frisoni et al, ).…”

Section: Introductionmentioning

confidence: 99%

Tractostorm: The what, why, and how of tractography dissection reproducibility

Rheault

Benedictis

Daducci

et al. 2020

Human Brain Mapping

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Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

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Section: Introductionmentioning

confidence: 99%

Tractostorm: The what, why, and how of tractography dissection reproducibility

Rheault

Benedictis

Daducci

et al. 2020

Human Brain Mapping

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“…They applied image enhancement and image segmentation as preprocessing steps and used a neural network for classification. Piccinini et al [14] used a fully automated mosaicing method to improve the reliability and reproducibility of live and dead cell counting. Mouelhi and colleagues [15] also described automatic image segmentation with active contour for stained nuclei in breast cancer tissue which could segment touching nuclei to get the total number of breast cancer nuclei.…”

Section: Introductionmentioning

confidence: 99%

Live and Dead Cells Counting from Microscopic Trypan Blue Staining Images using Thresholding and Morphological Operation Techniques

Aung

Kanokwiroon

Phairatana

et al. 2019

IJECE

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Cell counting is a required procedure in biomedical experiments and drug testing. Manual cell counting performed with a hemocytometer is time consuming and individual dependence. This study reportedthe development of a computer-assisted program for trypan blue stained-cell counting using digital image analysis. Images of trypan blue-stained breast cancer cells line were obtained by a microscope with a digital camera. Undesired noise and debris were removed by applying a guided image filter. Color space HSV (Hue, Saturation and Value)conversion and grayscale conversion were performed for distinguishing between live and dead cells. Image thresholding and morphological operators were applied for image segmentation. Live and dead cells were counted after image segmentation and the results were compared with manual counting by three well-experienced counters. The computer-assisted cell counting from thirty-six trypan blue-stained microscopic images had a high correlation coefficient with the live cell results of the experts (r=0.99). The correlation coefficient of the number of dead cells comparing the computer-assisted count and the experts’ count was 0.74. Our approach offers high accuracy (>85%)on counting live cells compared with the experts’ counting. This automated cell counting approach can assist biomedical researchers for both live and dead cells counting.

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“…While such manual annotation would be tedious, time consuming and even error prone, we consider this an indispensable step towards building a realistic and useful dataset for ML-based development. The need for such a gold standard that quantifies human variability is well-known in other fields, such as automatic image segmentation, cell counting or in machine learning [70,71,72,73]. Despite the fact that simulated brain images come with a pixel-accurate set of ground truth streamlines that can be generated in a matter of seconds, by definition synthetic diffusion signals are over-simplistic pictures of real data and, as such, cannot provide any guarantee of subsequent performance for data-driven methods on real data.…”

Section: Essential Characteristicsmentioning

confidence: 99%

Tractography and machine learning: Current state and open challenges

Poulin

Jörgens

Jodoin

et al. 2019

Magnetic Resonance Imaging

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Supervised machine learning (ML) algorithms have recentlybeen proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can be path-based and local-model-free, and easily incorporate anatomical priors to make contextual and non-local decisions that should help the tracking process. ML-based techniques have thus shown promising reconstructions of larger spatial extent of existing white matter bundles, promising reconstructions of less false positives, and promising robustness to known position and shape biases of current tractography techniques. But as of today, none of these ML-based methods have shown conclusive performances or have been adopted as a de facto solution to tractography. One reason for this might be the lack of well-defined and extensive frameworks to train, evaluate, and compare these methods.In this paper, we describe several datasets and evaluation tools that contain useful features for ML algorithms, along with the various methods proposed in the recent years.We then discuss the strategies that are used to evaluate and compare those methods, as well as their shortcomings. Finally, we describe the particular needs of ML tractography methods and discuss tangible solutions for future works. K E Y W O R D SDiffusion MRI, tractography, machine learning, benchmark 1 arXiv:1902.05568v1 [q-bio.NC]

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Improving reliability of live/dead cell counting through automated image mosaicing

Cited by 17 publications

References 80 publications

Tractostorm: The what, why, and how of tractography dissection reproducibility

Tractostorm: The what, why, and how of tractography dissection reproducibility

Live and Dead Cells Counting from Microscopic Trypan Blue Staining Images using Thresholding and Morphological Operation Techniques

Tractography and machine learning: Current state and open challenges

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