AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques

15Aim: this study, we established an artificial intelligence system for rapid 16 identification of fetal nucleated red blood cells (fNRBCs). 17Method: Density gradient centrifugation and magnetic-activated cell sorting were 18 used for the separation of fNRBCs from umbilical cord blood. The cell block 19 technique was used for fixation. We proposed a novel preprocessing method based on 20 imaging characteristics of fNRBCs for region of interest (ROI) extraction, which 21 automatically segmented individual cells in peripheral blood cell smears. The 22 discriminant information from ROIs was encoded into a feature vector and 23 pathological diagnosis were provided by the prediction network. 24Results: Four umbilical cord blood samples were collected and validated based on a 25 large dataset containing 260 samples. Finally, the dataset was classified into 3,720 and 26 1,040 slides for training and testing, respectively. In the test set, classifier obtained 27 98.5% accuracy and 96.5% sensitivity. 28Conclusion: Therefore, this study offers an effective and accurate method for 29 fNRBCs preservation and identification. 30 Keywords: fetal nucleated red blood cells, cell-block, deep learning, non-invasive 31 prenatal diagnosis 32 Introduction 33The clinical application of fNRBCs during pregnancy could be classified into two 34 main categories 1,2 . One is the prognosis of possible diseases in pregnant women by 35 counting fNRBCs in umbilical cord blood. Chronic tissue hypoxia results in increased 36 levels of erythropoietin, which, in turn, leads to stimulation of erythropoiesis and 37 increased numbers of circulating nucleated red blood cells (NRBCs) 1,3-5 . Increased 38 umbilical cord levels of erythropoietin have been reported in pregnancies complicated 39 by intrauterine growth restriction, maternal hypertension, preeclampsia, maternal 40 smoking, Rh isoimmunization, and maternal diabetes 5-8 . As expected, each of these 41 conditions has been associated with increased NRBCs in the newborn 9 . The other 42 objective is to screen and extract fNRBCs from maternal peripheral blood for 43 non-invasive prenatal diagnosis (NIPD) 10-12 . The choice of fNRBCs as ideal target 44 cells is based on the following parameters 13,14 : (1) presence of intact nuclei containing 45 the complete fetal genome in fNRBCs, which is a prerequisite for prenatal analysis; (2) 46 limited life span of fNRBCs in the maternal circulation, which can be differentiated 47 55 cells) 16,17 . Several fNRBC enrichment methods based on different principles have 56 been reported, such as density gradient centrifugation (DGC) 13,18 , 57 fluorescence-activated cell sorting (FACS) 19 , and magnetic-activated cell sorting 58 (MACS) 20 , dielectrophoresis, and microfluidics based technologies 21,22 . Nevertheless, 59 long-term preservation of samples and rapid identification of target cells (fNRBCs) 60 still present considerable challenges. 61 Since the identification of fNRBCs in large number of cell block slices represent a 62 huge manual burden on p...

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“…A semi-automated ROI extraction algorithm based on global threshold segmentation and watershed algorithm was proposed 32 .…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based adaptive detection of fetal nucleated red blood cells

Sun

Wang

Zhao

et al. 2020

Preprint

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“…A semi-automated ROI extraction algorithm based on global threshold segmentation and watershed algorithm was proposed 41 . First, a Gaussian low-pass filter was applied for image pre-processing.…”

Section: Cellular-level Region Of Interest (Roi) Extractionmentioning

confidence: 99%

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

Sun¹,

Wang²,

Zhao³

et al. 2021

Preprint

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Background: The rapid recognition of fetal nucleated red blood cells (fNRBCs) present considerable challenges.Objective: To establish a computer-aided diagnosis system (CAD) for rapid recognition of fNRBCs by a convolutional neural network (CNN). Methods: We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. A cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin stains. Then we proposed a CAD-based on CNN to automatically learn discriminative features and recognize fNRBCs. Region of interest 1 extraction methods were used to automatically segment individual cells in cell slices. The discriminant information from ROIs was encoded into a feature vector. The prediction network provided a pathological diagnosis. Results: Totally, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures) the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5% in the test set (1040 pictures).Conclusion: We present a CAD system for effective and accurate fNRBCs recognition based on CNN.

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“…Approaches proposed in [22][23][24][25][26] were tested on small bacterial colonies or microorganisms. Typically, bacteria produce colonies with a well-defined circular shape and adjacent colonies rarely merging together.…”

Section: Introductionmentioning

confidence: 99%

“…The processing pipeline exploits Principal Component Analysis (PCA) and Otsu's thresholding to extract Escherichia coli K-12 bacterial colonies [28]. In [25], Khan et al proposed an approach exploiting adaptive thresholding for colony detection and the watershed algorithm to separate merged colonies. The approach was tested on four different bacterial species including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus.…”

Section: Introductionmentioning

confidence: 99%

MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

et al. 2020

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A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.

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AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques

Cited by 50 publications

References 27 publications

Deep learning-based adaptive detection of fetal nucleated red blood cells

Deep learning-based adaptive detection of fetal nucleated red blood cells

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

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