1
|
|
2
|
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.
Collapse
Affiliation(s)
| | | | - Luís Rosado
- Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal.
| | | |
Collapse
|
3
|
William W, Ware A, Basaza-Ejiri AH, Obungoloch J. A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images. Biomed Eng Online 2019; 18:16. [PMID: 30755214 PMCID: PMC6373062 DOI: 10.1186/s12938-019-0634-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 02/05/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. METHOD Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. RESULTS The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. CONCLUSIONS The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.
Collapse
Affiliation(s)
- Wasswa William
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara, 1410 Uganda
| | - Andrew Ware
- Faculty of Computing, Engineering and Science, University of South Wales, Prifysgol, Pontypridd, UK
| | | | - Johnes Obungoloch
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Mbarara, 1410 Uganda
| |
Collapse
|
4
|
William W, Ware A, Basaza-Ejiri AH, Obungoloch J. Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.02.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
|
5
|
Singh SK, Goyal A. A Novel Approach to Segment Nucleus of Uterine Cervix Pap Smear Cells Using Watershed Segmentation. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/978-981-10-5780-9_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
|
6
|
Circular shape constrained fuzzy clustering (CiscFC) for nucleus segmentation in Pap smear images. Comput Biol Med 2017; 85:13-23. [DOI: 10.1016/j.compbiomed.2017.04.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 03/31/2017] [Accepted: 04/12/2017] [Indexed: 01/24/2023]
|
7
|
Bora K, Chowdhury M, Mahanta LB, Kundu MK, Das AK. Automated classification of Pap smear images to detect cervical dysplasia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 138:31-47. [PMID: 27886713 DOI: 10.1016/j.cmpb.2016.10.001] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 08/19/2016] [Accepted: 10/04/2016] [Indexed: 05/26/2023]
Abstract
BACKGROUND AND OBJECTIVES The present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades. METHODS The classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix. The system is evaluated on two generated databases obtained from two diagnostic centers, one containing 1610 single cervical cells and the other 1320 complete smear level images. The main objective of this database generation is to categorize the images according to the Bethesda system of classification both of which require lots of training and expertise. The system is also trained and tested on the benchmark Herlev University database which is publicly available. In this contribution a new segmentation technique has also been proposed for extracting shape features. Ripplet Type I transform, Histogram first order statistics and Gray Level Co-occurrence Matrix have been used for color and texture features respectively. To improve classification results, ensemble method is used, which integrates the decision of three classifiers. Assessments are performed using 5 fold cross validation. RESULTS Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods. CONCLUSION This type of automated cancer classifier will be of particular help in early detection of cancer.
Collapse
Affiliation(s)
- Kangkana Bora
- Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India.
| | - Manish Chowdhury
- KTH, School of Technology and Health, Hälsovägen 11c, SE-141 57 Huddinge, Stockholm, Sweden
| | - Lipi B Mahanta
- Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India
| | - Malay Kumar Kundu
- Department of Machine Intelligence Unit, Indian Statistical Institute, 203 B.T.Road, Kolkata 700108, India
| | - Anup Kumar Das
- Ayursundra Healthcare Pvt. Ltd, DMB Plaza, Lachit Nagar, Guwahati 781007, Assam, India
| |
Collapse
|
8
|
Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF. Comput Biol Med 2016; 71:46-56. [DOI: 10.1016/j.compbiomed.2016.01.025] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 01/10/2016] [Accepted: 01/22/2016] [Indexed: 11/19/2022]
|
9
|
A method for quantitative analysis of clump thickness in cervical cytology slides. Micron 2016; 80:73-82. [DOI: 10.1016/j.micron.2015.09.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 09/03/2015] [Accepted: 09/03/2015] [Indexed: 12/29/2022]
|
10
|
Branca M, Longatto-Filho A. Recommendations on Quality Control and Quality Assurance in Cervical Cytology. Acta Cytol 2015; 59:361-9. [PMID: 26569109 DOI: 10.1159/000441515] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 10/06/2015] [Indexed: 12/23/2022]
Abstract
OBJECTIVE We aimed to critically evaluate the importance of quality control (QC) and quality assurance (QA) strategies in the routine work of uterine cervix cytology. STUDY DESIGN We revised all the main principles of QC and QA that are already being implemented worldwide and then discussed the positive aspects and limitations of these as well as proposing alternatives when pertinent. RESULTS A literature review was introduced after highlighting the main historical revisions, and then a critical evaluation of the principal innovations in screening programmes was conducted, with recommendations being postulated. CONCLUSIONS Based on the analysed data, QC and QA are two essential arms that support the quality of a screening programme.
Collapse
Affiliation(s)
- Margherita Branca
- Unit of Cytopathology, National Centre of Epidemiology, Surveillance and Promotion of Health, National Institute of Health (ISS), Rome, Italy
| | | |
Collapse
|
11
|
Chankong T, Theera-Umpon N, Auephanwiriyakul S. Automatic cervical cell segmentation and classification in Pap smears. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:539-56. [PMID: 24433758 DOI: 10.1016/j.cmpb.2013.12.012] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 10/26/2013] [Accepted: 12/18/2013] [Indexed: 05/26/2023]
Abstract
Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts.
Collapse
Affiliation(s)
- Thanatip Chankong
- Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Nipon Theera-Umpon
- Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Biomedical Engineering Center, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Sansanee Auephanwiriyakul
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Biomedical Engineering Center, Chiang Mai University, Chiang Mai 50200, Thailand.
| |
Collapse
|
12
|
Zhang L, Kong H, Ting Chin C, Liu S, Fan X, Wang T, Chen S. Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytometry A 2013; 85:214-30. [PMID: 24376056 DOI: 10.1002/cyto.a.22407] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Revised: 09/27/2013] [Accepted: 10/05/2013] [Indexed: 11/08/2022]
Abstract
Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals.
Collapse
Affiliation(s)
- Ling Zhang
- Department of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen, 518060, China; Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound Imaging, Shenzhen, 518060, China
| | | | | | | | | | | | | |
Collapse
|