Overlapping Cell Segmentation of Cervical Cytology Images Based on Nuclear Radial Boundary Enhancement

Wang, Tao; Lan, Hong; Niu, Landing; Fan, Zhipeng; Yang, Guihua

doi:10.1155/2022/5969056

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…∗ The test dataset consists of 210 images of size extracted from real EDF images of size on ISBI2015. Methods DSC TPRp FPRp FNRo Tareef 9 0.85 ± NA 0.94 ± NA 0.002 ± NA 0.34 ± NA Song 5 0.89 ± NA 0.92 ± NA 0.002 ± NA 0.26 ± NA Lee 28 0.88 ± 0.09 0.88 ± 0.12 0.001 ± 0.001 0.43 ± 0.17 Phoulday 20 0.87±NA 0.88 ± NA NA 0.21 ± NA Wan* , 7 0.92 ± 0.05 0.91 ± 0.05 0.001 ± 0.003 0.24 ± 0.19 Wang 23 0.88 ± NA 0.85 ± NA NA 0.32 ± NA Ours 0.88 ± 0.02 0.92 ± 0.04 0.001 ± 0.0002 0.22 ± 0.12 …”

Section: Resultsmentioning

confidence: 99%

“…Song et al 22 constructed the segmentation by grouping contour fragments to form a closed boundary, where shape priors such as curvature information were used. Wang et al 23 proposed a segmentation algorithm based on the nuclear radial boundary enhancement for overlapping cells. Although deep learning based methods generally are computationally expensive, they outperform other algorithms in many computer vision tasks.…”

Section: Related Workmentioning

confidence: 99%

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An improved approach for automated cervical cell segmentation with PointRend

Zhang,

Wang,

Zhao

et al. 2024

Sci Rep

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Regular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential task because it can present better understanding of the characteristics of cervical cells. The main challenge of cell cytoplasm segmentation is that many boundaries in cell clumps are extremely difficult to be identified. This paper proposes a new convolutional neural network based on Mask RCNN and PointRend module, to segment overlapping cervical cells. The PointRend head concatenates fine grained features and coarse features extracted from different feature maps to fine-tune the candidate boundary pixels of cell cytoplasm, which are crucial for precise cell segmentation. The proposed model achieves a 0.97 DSC (Dice Similarity Coefficient), 0.96 TPRp (Pixelwise True Positive Rate), 0.007 FPRp (Pixelwise False Positive Rate) and 0.006 FNRo (Object False Negative Rate) on dataset from ISBI2014. Specially, the proposed method outperforms state-of-the-art result by about $$3\%$$ 3 % on DSC, $$1\%$$ 1 % on TPRp and $$1.4\%$$ 1.4 % on FNRo respectively. The performance metrics of our model on dataset from ISBI2015 are slight better than the average value of other approaches. Those results indicate that the proposed method could be effective in cytological analysis and then help experts correctly discover cervical cell lesions

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An improved approach for automated cervical cell segmentation with PointRend

Zhang,

Wang,

Zhao

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This type of segmentation can recognize and distinguish every cell in a microscopic image, even those in contact [15]. Segmentation is used to detect cytoplasm and nucleus on pap smear images [16], [17] and detect ROI (regions of interest), which is the basis of the automatic cervical cancer screening system. Effective segmentation can facilitate the extraction of meaningful information and simplify image data for further analysis [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images

Merlina

Noersasongko

Andono

et al. 2023

JOIV : Int. J. Inform. Visualization

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The complexity of the cell structure and high overlap cause poor image contrast. Complex imaging factors can make automatic visual interpretation more difficult. Segmentation separates a digital image into different parts with homogeneous attributes so that different areas have different features. The challenges faced in performing nucleus segmentation on Pap Smear (PS) images are poor contrast, the presence of neutrophils, and uneven staining of overlapping cells. This research was conducted to improve image quality in identifying the nucleus accurately. The method used is the Polynomial Contrast Enhancement (PCE) model as an approach to preprocessing. This method functions to change the contrast of the Pap smear image against the overlapping cells so that it becomes a significant contrast in detecting the edge of the nucleus object. The detection process uses the Robert and Prewitt edge detection method to test the identification of the nucleus object on 797 PS Repository images of the University of Nusa Mandiri (RepomedUNM). The accuracy result obtained is 86.8%. Comparing Robert's edge detection and Prewitt's edge detection shows that the PCE approach as a filter method can overcome color contrast problems and detect more accurately. The difficulty in detecting the nucleus from the PS image against the overlapping cells can be solved. This method can distinguish overlapping cells from their core during testing, thus becoming a reference in identifying cells with improved accuracy and testing larger data sets.

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Overlapping Cell Segmentation of Cervical Cytology Images Based on Nuclear Radial Boundary Enhancement

Cited by 2 publications

References 33 publications

An improved approach for automated cervical cell segmentation with PointRend

An improved approach for automated cervical cell segmentation with PointRend

Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images

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