Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework

Bergmeir, Christoph; Silvente, Miguel García; Benítez, José Manuel

doi:10.1016/j.cmpb.2011.09.017

Cited by 113 publications

(71 citation statements)

References 30 publications

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“…Active contour model (Bamford and Lovell, 1998), morphology reconstruction (Plissiti et al, 2011), and level set (Bergmeir et al, 2012) are the most commonly used techniques for the accurate detection of nucleus boundary. To date, the segmentation of normal cervical nuclei has already achieved high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Graph-based segmentation of abnormal nuclei in cervical cytology

Zhang

Kong

Liu

et al. 2017

Computerized Medical Imaging and Graphics

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A general method is reported for improving the segmentation of abnormal cell nuclei in cervical cytology images. In automation-assisted reading of cervical cytology, one of the essential steps is the segmentation of nuclei. Despite some progress, there is a need to improve the sensitivity, particularly the segmentation of abnormal nuclei. Our method starts with pre-segmenting the nucleus to define the coarse center and size of nucleus, which is used to construct a graph by image unfolding that maps ellipse-like border in the Cartesian coordinate system to lines in the polar coordinate system. The cost function jointly reflects properties of nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are utilized to modify the local cost functions. The globally optimal path in the constructed graph is then identified by dynamic programming with an iterative approach ensuring an optimal closed contour. Validation of our method was performed on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained manual liquid-based cytology (HEMLBC). Compared with five state-of-the-art approaches, our graph-search based method shows superior performance.

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Section: Introductionmentioning

confidence: 99%

Graph-based segmentation of abnormal nuclei in cervical cytology

Zhang

Kong

Liu

et al. 2017

Computerized Medical Imaging and Graphics

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“…active contours, level-sets and genetic algorithms. Examples include segmenting epithelial cells (both cytoplasm and nucleus) using level-sets [7], segmenting breast cell nuclei using color space transformations and k-means clustering [8], segmenting breast cell nuclei using thresholding on the red color channel and level-sets [9] and segmenting cervical cell nuclei using mean-shift filtering, edge extraction, ellipse fitting and level-sets [10]. Despite the large number of existing proposals for cell nuclei segmentation in cytology, few has considered cell nuclei segmentation in lung cytology.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology

Forsberg

Monsef²

2014

2014 22nd International Conference on Pattern Recognition

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This paper presents results from an evaluation of three previously presented methods for segmentation of cell nuclei in lung cytology samples scanned by whole-slide scanners. Wholeslide images from seven cases of endobronchial ultrasound-guided transbronchial needle aspiration samples were used for extracting a number of regions of interest, in which approximately 2700 cell nuclei were manually segmented to form the ground truth. The segmented cells included benign bronchial epithelium, lymphocytes, granulocytes, histiocytes and malignant epithelial cells. The best results were obtained with a method based upon adaptive thresholding and an added step of clustering for distinguishing between cytoplasm and cell nuclei. This method achieved a mean DICE-score of 0.81 and a sensitivity and specificity of 0.88 and 0.81 respectively. In addition, this method was by far the fastest method, with a mean processing time of 7.8 s per image (2 048 × 2 048 pixels per image). By further improvements, such as lowering the false positive rate and using parallel computing hardware, this method has the potential to form the first building block in a system for computerized screening of whole-slide images in lung cytology.

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“…Basavanhally et al [14] have used Active Contour model based on a color gradient with a hierarchical normalized cut for segmenting breast cancer images and obtained segmentation accuracy 89%. Bergmeir et al [15] have used voting scheme and prior knowledge for nuclei detection and elliptical shape prior to cytoplasm segmentation in Pap smear images of cervical cells and have attained TPR of 95.63%. Nosrati et al [16] have used starshaped prior using directional directives for segmenting cervical cells in ISBI 2014 challenge dataset and DC obtained was them was 0.88.…”

Section: Segmentationmentioning

confidence: 99%

Segmentation and Analysis of Diagnostic Images for Lesion Detection: Current Status and Future Potential

Bhattacharjee¹

2018

RMES

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In this study, we have presented an elaborate overview of various segmentation techniques found in the current medical literature for computeraided detection of lesions from different diagnostic imaging modalities. Lesions can be broadly classified as benign or malignant. Computer-aided detection of lesions solely depends on the diagnostic modality. Analysis of medical images for detection of lesions requires a high degree of accuracy and precision. Diagnostic imaging modalities can be broadly classified as invasive, non-invasive and minimally invasive. Each of the modalities has its own challenges. From each of the modalities, we have considered some challenging hazardous diseases. Amongst invasive techniques, microscopic images of the breast and cervical cancers were considered for the study. In non-invasive technique ultrasound images of breast cancer were considered and in minimally invasive technique colorectal cancer detection using colonoscopy has been considered.

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Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework

Cited by 113 publications

References 30 publications

Graph-based segmentation of abnormal nuclei in cervical cytology

Graph-based segmentation of abnormal nuclei in cervical cytology

Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology

Segmentation and Analysis of Diagnostic Images for Lesion Detection: Current Status and Future Potential

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