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Zhang B, Wang W, Zhao W, Jiang X, Patnaik LM. An improved approach for automated cervical cell segmentation with PointRend. Sci Rep 2024; 14:14210. [PMID: 38902285 PMCID: PMC11189924 DOI: 10.1038/s41598-024-64583-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/11/2024] [Indexed: 06/22/2024] Open
Abstract
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 % on DSC, 1 % on TPRp and 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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Affiliation(s)
- Baocan Zhang
- Chengyi College, Jimei University, Xiamen, 361021, Fujian, China
| | - Wenfeng Wang
- Shanghai Institute of Technology, Shanghai, 200235, China.
- London Institute of Technology, International Academy of Visual Art and Engineering, London, CR2 6EQ, UK.
| | - Wei Zhao
- Chengyi College, Jimei University, Xiamen, 361021, Fujian, China
| | - Xiaolu Jiang
- Chengyi College, Jimei University, Xiamen, 361021, Fujian, China
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Yang X, Ding B, Qin J, Guo L, Zhao J, He Y. HVS-Unsup: Unsupervised cervical cell instance segmentation method based on human visual simulation. Comput Biol Med 2024; 171:108147. [PMID: 38387385 DOI: 10.1016/j.compbiomed.2024.108147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/22/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Instance segmentation plays an important role in the automatic diagnosis of cervical cancer. Although deep learning-based instance segmentation methods can achieve outstanding performance, they need large amounts of labeled data. This results in a huge consumption of manpower and material resources. To solve this problem, we propose an unsupervised cervical cell instance segmentation method based on human visual simulation, named HVS-Unsup. Our method simulates the process of human cell recognition and incorporates prior knowledge of cervical cells. Specifically, firstly, we utilize prior knowledge to generate three types of pseudo labels for cervical cells. In this way, the unsupervised instance segmentation is transformed to a supervised task. Secondly, we design a Nucleus Enhanced Module (NEM) and a Mask-Assisted Segmentation module (MAS) to address problems of cell overlapping, adhesion, and even scenarios involving visually indistinguishable cases. NEM can accurately locate the nuclei by the nuclei attention feature maps generated by point-level pseudo labels, and MAS can reduce the interference from impurities by updating the weight of the shallow network through the dice loss. Next, we propose a Category-Wise droploss (CW-droploss) to reduce cell omissions in lower-contrast images. Finally, we employ an iterative self-training strategy to rectify mislabeled instances. Experimental results on our dataset MS-cellSeg, the public datasets Cx22 and ISBI2015 demonstrate that HVS-Unsup outperforms existing mainstream unsupervised cervical cell segmentation methods.
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Affiliation(s)
- Xiaona Yang
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Bo Ding
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Jian Qin
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Luyao Guo
- Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China
| | - Jing Zhao
- Northeast Forestry University, School of Mechanical and Electrical Engineering, Harbin, 150040, China
| | - Yongjun He
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, China.
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3
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Song Y, Zhang A, Zhou J, Luo Y, Lin Z, Zhou T. Overlapping cytoplasms segmentation via constrained multi-shape evolution for cervical cancer screening. Artif Intell Med 2024; 148:102756. [PMID: 38325933 DOI: 10.1016/j.artmed.2023.102756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 12/03/2023] [Accepted: 12/29/2023] [Indexed: 02/09/2024]
Abstract
Segmenting overlapping cytoplasms in cervical smear images is a clinically essential task for quantitatively measuring cell-level features to screen cervical cancer This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region Although shape prior-based models that compensate intensity deficiency by introducing prior shape information about cytoplasm are firmly established, they often yield visually implausible results, as they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm In this paper, we present an effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors We model local shape priors (cytoplasm-level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump-level) modeled by considering mutual shape constraints of cytoplasms in the clump We also constrain the resulting shape in each evolution to be in the built shape hypothesis set for further reducing implausible segmentation results We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results confirm its effectiveness.
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Affiliation(s)
- Youyi Song
- School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Ao Zhang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Jinglin Zhou
- School of Philosophy, Fudan University, Shanghai, 200433, China
| | - Yu Luo
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China
| | - Zhizhe Lin
- School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
| | - Teng Zhou
- School of Cyberspace Security, Hainan University, Haikou, 570228, China.
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4
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Rasheed A, Shirazi SH, Umar AI, Shahzad M, Yousaf W, Khan Z. Cervical cell's nucleus segmentation through an improved UNet architecture. PLoS One 2023; 18:e0283568. [PMID: 37788295 PMCID: PMC10547184 DOI: 10.1371/journal.pone.0283568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 03/11/2023] [Indexed: 10/05/2023] Open
Abstract
Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other techniques. We have proposed a deep learning model, namely C-UNet (Cervical-UNet), to segment cervical nuclei from overlapped, fuzzy, and blurred cervical cell smear images. Cross-scale features integration based on a bi-directional feature pyramid network (BiFPN) and wide context unit are used in the encoder of classic UNet architecture to learn spatial and local features. The decoder of the improved network has two inter-connected decoders that mutually optimize and integrate these features to produce segmentation masks. Each component of the proposed C-UNet is extensively evaluated to judge its effectiveness on a complex cervical cell dataset. Different data augmentation techniques were employed to enhance the proposed model's training. Experimental results have shown that the proposed model outperformed extant models, i.e., CGAN (Conditional Generative Adversarial Network), DeepLabv3, Mask-RCNN (Region-Based Convolutional Neural Network), and FCN (Fully Connected Network), on the employed dataset used in this study and ISBI-2014 (International Symposium on Biomedical Imaging 2014), ISBI-2015 datasets. The C-UNet achieved an object-level accuracy of 93%, pixel-level accuracy of 92.56%, object-level recall of 95.32%, pixel-level recall of 92.27%, Dice coefficient of 93.12%, and F1-score of 94.96% on complex cervical images dataset.
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Affiliation(s)
- Assad Rasheed
- Department of Computer Science & Information Technology, Hazara University Mansehra, Mansehra, Pakistan
| | - Syed Hamad Shirazi
- Department of Computer Science & Information Technology, Hazara University Mansehra, Mansehra, Pakistan
| | - Arif Iqbal Umar
- Department of Computer Science & Information Technology, Hazara University Mansehra, Mansehra, Pakistan
| | - Muhammad Shahzad
- Department of Computer Science & Information Technology, Hazara University Mansehra, Mansehra, Pakistan
| | - Waqas Yousaf
- Department of Computer Science & Information Technology, Hazara University Mansehra, Mansehra, Pakistan
| | - Zakir Khan
- Department of Computer Science & Information Technology, Hazara University Mansehra, Mansehra, Pakistan
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Kupas D, Harangi B. Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1452-1455. [PMID: 36083935 DOI: 10.1109/embc48229.2022.9871171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision support systems and automated analysis tools of Pap-smears often use these kinds of manually extracted, global features based on clinical expert opinion. In this paper, a solution is introduced where 29 different contextual features are combined with local features learned by a neural network so that it increases classification performance. The weight distribution between the features is also investigated leading to a conclusion that the numerical features are indeed forming an important part of the learning process. Furthermore, extensive testing of the presented methods is done using a dataset annotated by clinical experts. An increase of 3.2% in F1-Score value can be observed when using the combination of contextual and local features. Clinical Relevance - Analysis of images extracted from digital Pap-test using modern machine learning tools is discussed in many scientific papers. The manual classification of the cells can be time-consuming and expensive which requires a high amount of manual labor. Furthermore the result of the manual classification can also be uncertain due to interobserver variability. Considering these, any result that can lead to a more reliable highly accurate classification method is considered valuable in the field of cervical cancer screening.
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Liu J, Fan H, Wang Q, Li W, Tang Y, Wang D, Zhou M, Chen L. Local Label Point Correction for Edge Detection of Overlapping Cervical Cells. Front Neuroinform 2022; 16:895290. [PMID: 35645753 PMCID: PMC9133536 DOI: 10.3389/fninf.2022.895290] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/20/2022] [Indexed: 11/18/2022] Open
Abstract
Accurate labeling is essential for supervised deep learning methods. However, it is almost impossible to accurately and manually annotate thousands of images, which results in many labeling errors for most datasets. We proposes a local label point correction (LLPC) method to improve annotation quality for edge detection and image segmentation tasks. Our algorithm contains three steps: gradient-guided point correction, point interpolation, and local point smoothing. We correct the labels of object contours by moving the annotated points to the pixel gradient peaks. This can improve the edge localization accuracy, but it also causes unsmooth contours due to the interference of image noise. Therefore, we design a point smoothing method based on local linear fitting to smooth the corrected edge. To verify the effectiveness of our LLPC, we construct a largest overlapping cervical cell edge detection dataset (CCEDD) with higher precision label corrected by our label correction method. Our LLPC only needs to set three parameters, but yields 30–40% average precision improvement on multiple networks. The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection. We hope that our study will give a strong boost to the development of the label correction for edge detection and image segmentation. We will release the dataset and code at: https://github.com/nachifur/LLPC.
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Affiliation(s)
- Jiawei Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Huijie Fan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- *Correspondence: Huijie Fan
| | - Qiang Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Key Laboratory of Manufacturing Industrial Integrated, Shenyang University, Shenyang, China
| | - Wentao Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Yandong Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Danbo Wang
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
- Danbo Wang
| | - Mingyi Zhou
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Li Chen
- Department of Pathology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
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7
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Classifying nanostructured and heterogeneous materials from transmission electron microscopy images using convolutional neural networks. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07029-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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8
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Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:3890988. [PMID: 34646333 PMCID: PMC8505098 DOI: 10.1155/2021/3890988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/18/2021] [Indexed: 11/18/2022]
Abstract
The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in segmenting complex medical images. We present a two-stage framework based on Mask RCNN to automatically segment overlapping cells. In stage one, candidate cytoplasm bounding boxes are proposed. In stage two, pixel-to-pixel alignment is used to refine the boundary and category classification is also presented. The performance of the proposed method is evaluated on publicly available datasets from ISBI 2014 and 2015. The experimental results demonstrate that our method outperforms other state-of-the-art approaches with DSC 0.92 and FPRp 0.0008 at the DSC threshold of 0.8. Those results indicate that our Mask RCNN-based segmentation method could be effective in cytological analysis.
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Victória Matias A, Atkinson Amorim JG, Buschetto Macarini LA, Cerentini A, Casimiro Onofre AS, De Miranda Onofre FB, Daltoé FP, Stemmer MR, von Wangenheim A. What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review. Comput Med Imaging Graph 2021; 91:101934. [PMID: 34174544 DOI: 10.1016/j.compmedimag.2021.101934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/16/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022]
Abstract
Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.
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Affiliation(s)
- André Victória Matias
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil.
| | | | | | - Allan Cerentini
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil.
| | | | | | - Felipe Perozzo Daltoé
- Department of Pathology, Federal University of Santa Catarina, Florianópolis, Brazil.
| | - Marcelo Ricardo Stemmer
- Automation and Systems Department, Federal University of Santa Catarina, Florianópolis, Brazil.
| | - Aldo von Wangenheim
- Brazilian Institute for Digital Convergence, Federal University of Santa Catarina, Florianópolis, Brazil.
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10
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Hoque IT, Ibtehaz N, Chakravarty S, Rahman MS, Rahman MS. A contour property based approach to segment nuclei in cervical cytology images. BMC Med Imaging 2021; 21:15. [PMID: 33509110 PMCID: PMC7841885 DOI: 10.1186/s12880-020-00533-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 12/06/2020] [Indexed: 11/14/2022] Open
Abstract
Background Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts. Methods After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value. Results We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset. Conclusion We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.
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Affiliation(s)
- Iram Tazim Hoque
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, Bangladesh
| | - Nabil Ibtehaz
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, Bangladesh
| | - Saumitra Chakravarty
- Department of Pathology, Bangabandhu Sheikh Mujib Medical University, Shahabag, Dhaka, Bangladesh
| | - M Saifur Rahman
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, Bangladesh
| | - M Sohel Rahman
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, Bangladesh.
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Huang J, Wang T, Zheng D, He Y. Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm. Bioengineered 2020; 11:484-501. [PMID: 32279589 PMCID: PMC7161549 DOI: 10.1080/21655979.2020.1747834] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results.
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Affiliation(s)
- Jinjie Huang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin, China.,School of Computer Science, Harbin University of Science and Technology, Harbin, China
| | - Tao Wang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin, China.,School of Computer Science, Harbin University of Science and Technology, Harbin, China.,Network and Education Technology Center, Harbin University of Commerce, Harbin, China
| | - Dequan Zheng
- Network and Education Technology Center, Harbin University of Commerce, Harbin, China
| | - Yongjun He
- School of Computer Science, Harbin University of Science and Technology, Harbin, China
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12
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Polar coordinate sampling-based segmentation of overlapping cervical cells using attention U-Net and random walk. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.12.036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.06.086] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Saha R, Bajger M, Lee G. Segmentation of cervical nuclei using SLIC and pairwise regional contrast. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:3422-3425. [PMID: 30441123 DOI: 10.1109/embc.2018.8513021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A framework to detect and segment nuclei from cervical cytology images is proposed in this study. Poor contrast, spurious edges, degree of overlap, and intensity inhomogeneity make the nuclei segmentation task more complex in overlapping cell images. The proposed technique segments cervical nuclei by merging over-segmented SLIC superpixel regions using a novel region merging criteria based on pairwise regional contrast and image gradient contour evaluations. The framework was evaluated using the first overlapping cervical cytology image segmentation challenge - ISBI 2014 dataset. The result shows that the proposed framework outperforms the state-of-the-art algorithms in nucleus detection and segmentation accuracies.
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15
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Conceição T, Braga C, Rosado L, Vasconcelos MJM. A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification. Int J Mol Sci 2019; 20:E5114. [PMID: 31618951 PMCID: PMC6834130 DOI: 10.3390/ijms20205114] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/07/2019] [Accepted: 10/09/2019] [Indexed: 02/07/2023] Open
Abstract
Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions.
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Affiliation(s)
| | | | - Luís Rosado
- Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal.
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16
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Ahmady Phoulady H, Goldgof D, Hall LO, Mouton PR. Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex. J Chem Neuroanat 2019; 98:1-7. [DOI: 10.1016/j.jchemneu.2019.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 02/23/2019] [Accepted: 02/26/2019] [Indexed: 10/27/2022]
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17
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Automatic cell nuclei segmentation and classification of cervical Pap smear images. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.09.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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