Automated Cell Nuclei Segmentation in Pleural Effusion Cytology Using Active Appearance Model

Baykal, Elif; Doğan, Hülya; Ekinci, Murat; Erçin, Mustafa Emre; Ersöz, Şafak

doi:10.1007/978-3-319-64698-5_6

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…[22] Baykal et al used the technique of active appearance model for to achieve efective cell seymentation from cytopatholoyical imayes, with yood diaynostic accuracy. [23] Te wavelet transform has also been shown to achieve a hiyh recoynition ratio. [24] Zhany et al have used morphometric parameters (area rate of the karyon and cytoplasm, the optic density, the shape factor) and used these parameters with a fuzzy patern recoynition model to detect cancer cells.…”

Section: Discussionmentioning

confidence: 99%

A machine learning model for screening of body fluid cytology smears

Sanyal¹,

Paul²,

Rana

et al. 2021

Preprint

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Introduction: Body fluid cytology is one of the commonest investigations performed in indoor patients, both for diagnosis of suspected carcinoma as well as staging of known carcinoma. Carcinoma is diagnosed in body fluids by the pathologist through microscopic examination and searching for malignant epithelial cell clusters. The process of screening body fluid smears is a time consuming and error prone process. Aim: We have attempted to construct a machine learning model which can screen body fluid cytology smears for malignant cells. Materials and methods: MGG stained Ascitic / pleural fluid cytology smears were included from 21 cases (14 malignant, 07 benign) in this study. A total of 693 microphotographs were taken at 40x magnification at the same illumination and after correction of white balance. A Magnus Microphotography system was used for photography. The images were split into the training set (195 images), test set (120 images) and validation set (378 images). A machine learning model, a convolutional neural network, was developed in the Python programming language using the Keras deep learning library. The model was trained with the images of the training set. After completion of training, the model was evaluated on the test set of images. Results: Evaluation of the model on the test set produced a sensitivity of 97.87%, specificity 85.26%, PPV 95.18%, NPV 93.10% In 06 images, the model has failed to detect singly scattered malignant cells/ clusters. 14 (3.7%) false positives was reported by the model. The machine learning model shows potential utility as a screening tool. However, it needs improvement in detecting singly scattered malignant cells and filtering inflammatory infiltrate.

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Section: Discussionmentioning

confidence: 99%

A machine learning model for screening of body fluid cytology smears

Sanyal¹,

Paul²,

Rana

et al. 2021

Preprint

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show abstract

“…Few researchers have studied the automated segmentation of cells or nuclei in CPE images. E. Baykal et al 2017 [ 13 ] introduced an active appearance model to segment nuclei from the background in CPE images and compared it with color thresholding, clustering, and graph-based methods. They obtained 98.77% accuracy.…”

Section: Methodsmentioning

confidence: 99%

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

Win

Choomchuay

Hamamoto

et al. 2018

BioMed Research International

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Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

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Artificial intelligence-based techniques for analysis of body cavity fluids: a review

Mir

Sarwar

2021

Artif Intell Rev

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Automated Cell Nuclei Segmentation in Pleural Effusion Cytology Using Active Appearance Model

Cited by 3 publications

References 16 publications

A machine learning model for screening of body fluid cytology smears

A machine learning model for screening of body fluid cytology smears

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

Artificial intelligence-based techniques for analysis of body cavity fluids: a review

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