Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images

Win, Khin Yadanar; Choomchuay, Somsak; Hamamoto, Kazuhiko; Raveesunthornkiat, Manasanan

doi:10.1155/2018/9240389

Cited by 20 publications

(10 citation statements)

References 41 publications

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“…Recently, we collected more images and built a new dataset containing 35 CPE images. Using that new dataset, we employed twelve segmentation methods: (1) the Otsu method, (2) an ISODATA thresholding method, (3) a maximum entropy thresholding method, (4) cross-entropy thresholding, (5) minimum error thresholding, (6) fuzzy entropy thresholding, (7) adaptive thresholding, (8) K-Means clustering, (9) fuzzy C-means clustering, (10) mean shift clustering, (11) Chan-Vese level set, and (12) graph cut methods to extract the cell nuclei from CPE images, and we compared the results attained [ 18 ]. From the comparison results, Otsu, K-Means, mean shift clustering, graph cut method, and a Chan-Vese level set method provided promising segmentation results.…”

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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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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“…Most of them are based on machine learning techniques. Particular examples are automated cell nuclei segmentation [126], detection of cancer cells [127], [128], segmentation, and isolation of touching nuclei [129]. Next, the nucleus segmentation of breast fine-needle aspiration cytology (FNAC) images will be a handy field in this regard [130]- [133].…”

Section: Potential Related Fields For the Application Of Similar Smentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

101

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Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

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“…Previous studies have also investigated nucleus segmentation. For example, the watershed algorithm [9], K-means clustering [10], and Otsu's algorithm [11] are three well-known algorithms for nucleus segmentation. Unfortunately, these algorithms are less accurate for nucleus segmentation because they are sensitive to different parameter settings.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Blur Attention Network for Cell Nuclei Segmentation

Tuấn

Hsu

et al. 2022

Sensors

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Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task. In this work, we present a new deep learning-based method for cell nucleus segmentation. The proposed convolutional blur attention (CBA) network consists of downsampling and upsampling procedures. A blur attention module and a blur pooling operation are used to retain the feature salience and avoid noise generation in the downsampling procedure. A pyramid blur pooling (PBP) module is proposed to capture the multi-scale information in the upsampling procedure. The superiority of the proposed method has been compared with a few prior segmentation models, namely U-Net, ENet, SegNet, LinkNet, and Mask RCNN on the 2018 Data Science Bowl (DSB) challenge dataset and the multi-organ nucleus segmentation (MoNuSeg) at MICCAI 2018. The Dice similarity coefficient and some evaluation matrices, such as F1 score, recall, precision, and average Jaccard index (AJI) were used to evaluate the segmentation efficiency of these models. Overall, the proposal method in this paper has the best performance, the AJI indicator on the DSB dataset and MoNuSeg is 0.8429, 0.7985, respectively.

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Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images

Cited by 20 publications

References 41 publications

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

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

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Convolutional Blur Attention Network for Cell Nuclei Segmentation

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