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Image Analysis in Support of Computer-Assisted Cervical Cancer Screening
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cervical cancer is a disease that annually claims the lives of over a quarter of a million women. A substantial number of these deaths could be prevented if population wide cancer screening, based on the Papanicolaou test, were globally available. The Papanicolaou test involves a visual review of cellular material obtained from the uterine cervix. While being relatively inexpensive from a material standpoint, the test requires highly trained cytology specialists to conduct the analysis. There is a great shortage of such specialists in developing countries, causing these to be grossly overrepresented in the mortality statistics. For the last 60 years, numerous attempts at constructing an automated system, able to perform the screening, have been made. Unfortunately, a cost-effective, automated system has yet to be produced.

In this thesis, a set of methods, aimed to be used in the development of an automated screening system, are presented. These have been produced as part of an international cooperative effort to create a low-cost cervical cancer screening system. The contributions are linked to a number of key problems associated with the screening: Deciding which areas of a specimen that warrant analysis, delineating cervical cell nuclei, rejecting artefacts to make sure that only cells of diagnostic value are included when drawing conclusions regarding the final diagnosis of the specimen. Also, to facilitate efficient method development, two methods for creating synthetic images that mimic images acquired from specimen are described.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. , 95 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1106
Keyword [en]
Image analysis, cervical cancer, pap-smear, synthetic images, screening, image processing, cytometry
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-212518ISBN: 978-91-554-8828-4 (print)OAI: oai:DiVA.org:uu-212518DiVA: diva2:678172
Public defence
2014-02-07, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
Vinnova, 2008-01712Swedish Research Council, 2008-2738
Available from: 2014-01-16 Created: 2013-12-11 Last updated: 2014-07-21
List of papers
1. Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening
Open this publication in new window or tab >>Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening
2009 (English)In: Advances in Visual Computing / [ed] George Bebis et al., Berlin / Heidelberg: Springer , 2009, 337-346 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we describe a nuclei segmentation algorithm for Pap smears that uses anisotropic dilation for curve closing. Edge detection methods often return broken edges that need to be closed to achieve a proper segmentation. Our method performs dilation using Riemannian distance maps that are derived from the local structure tensor field in the image. We show that our curve closing improve the segmentation along weak edges and significantly increases the overall performance of segmentation. This is validated in a thorough study on realistic synthetic cell images from our Pap smear simulator. The algorithm is also demonstrated on bright-field microscope images of real Pap smears from cervical cancer screening.

Place, publisher, year, edition, pages
Berlin / Heidelberg: Springer, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5875
Keyword
Pap-smears, Riemannian dilation, Curve closing, Anisotropic dilation, Cell segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-111501 (URN)10.1007/978-3-642-10331-5_32 (DOI)978-3-642-10330-8 (ISBN)
Conference
ISVC
Available from: 2009-12-16 Created: 2009-12-16 Last updated: 2014-01-24Bibliographically approved
2. PAPSYNTH: Simulated Bright-field Images of Cervical Smears
Open this publication in new window or tab >>PAPSYNTH: Simulated Bright-field Images of Cervical Smears
2010 (English)In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a simulator for bright-field microscope imagesof ”Pap-smears”, which is the most common technique usedtoday for cervical cancer screening. Lacking a ground truth for realimages, these realistic synthetic images may be used to tune and validateimage analysis and processing algorithms. We demonstrate thisfor two tasks: uncorrelated noise removal and nucleus segmentation.The simulator is a part of a larger project, aiming at automatic, costefficient screening for cervical cancer in developing countries.In this paper, we present a simulator for bright-field microscope imagesof ”Pap-smears”, which is the most common technique usedtoday for cervical cancer screening. Lacking a ground truth for realimages, these realistic synthetic images may be used to tune and validateimage analysis and processing algorithms. We demonstrate thisfor two tasks: uncorrelated noise removal and nucleus segmentation.The simulator is a part of a larger project, aiming at automatic, costefficient screening for cervical cancer in developing countries.

Series
Biomedical Imaging: From Nano to Macro, ISSN 1945-7936 ; 7
Keyword
Synthetic cell images, bright-field, cervical cancer
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-126066 (URN)978-1-4244-4126-6 (ISBN)
Conference
ISBI 2010
Available from: 2010-06-02 Created: 2010-06-02 Last updated: 2017-02-08Bibliographically approved
3. Debris removal in Pap-smear images
Open this publication in new window or tab >>Debris removal in Pap-smear images
Show others...
2013 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 111, no 1, 128-138 p.Article in journal (Refereed) Published
Abstract [en]

Since its introduction in the 1940s the Pap-smear test has helped reduce the incidence of cervical cancer dramatically in countries where regular screening is standard. The automation of this procedure is an open problem that has been ongoing for over fifty years without reaching satisfactory results. Existing systems are discouragingly expensive and yet they are only able to make a correct distinction between normal and abnormal samples in a fraction of cases. Therefore, they are limited to acting as support for the cytotechnicians as they perform their manual screening. The main reason for the current limitations is that the automated systems struggle to overcome the complexity of the cell structures. Samples are covered in artefacts such as blood cells, overlapping and folded cells, and bacteria, that hamper the segmentation processes and generate large number of suspicious objects. The classifiers designed to differentiate between normal cells and pre-cancerous cells produce unpredictable results when classifying artefacts. In this paper, we propose a sequential classification scheme focused on removing unwanted objects, debris, from an initial segmentation result, intended to be run before the actual normal/abnormal classifier. The method has been evaluated using three separate datasets obtained from cervical samples prepared using both the standard Pap-smear approach as well as the more recent liquid based cytology sample preparation technique. We show success in removing more than 99% of the debris without loosing more than around one percent of the epithelial cells detected by the segmentation process.

Keyword
Debris removal, Pap-smear, Cervical cancer screening, LBC
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-204092 (URN)10.1016/j.cmpb.2013.02.008 (DOI)000320346400013 ()
Available from: 2013-07-22 Created: 2013-07-22 Last updated: 2017-12-06Bibliographically approved
4. Cluster detection and field-of-view quality rating: Applied to automated Pap-smear analysis
Open this publication in new window or tab >>Cluster detection and field-of-view quality rating: Applied to automated Pap-smear analysis
2013 (English)In: Proc. 2nd International Conference on Pattern Recognition Applications and Methods, SciTePress, 2013, 355-364 p.Conference paper, Published paper (Refereed)
Abstract [en]

Automated cervical cancer screening systems require high resolution analysis of a large number of epithelial cells, involving complex algorithms, mainly analysing the shape and texture of cell nuclei. This can be a very time consuming process. An initial selection of relevant fields-of-view in low resolution images could limit the number of fields to be further analysed at a high resolution. In particular, the detection of cell clusters is of interest for nuclei segmentation improvement, and for diagnostic purpose, malignant and endometrial cells being more prone to stick together in clusters than other cells. In this paper, we propose methods aiming at evaluating the quality of fields-of-view in bright-field microscope images of cervical cells. The approach consists in the construction of neighbourhood graphs using the nuclei as the set of vertices. Transformations are then applied on such graphs in order to highlight the main structures in the image. The methods result in the delineation of regions with varying cell density and the identification of cell clusters. Clustering methods are evaluated using a dataset of manually delineated clusters and compared to a related work.

Place, publisher, year, edition, pages
SciTePress, 2013
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-212509 (URN)978-989-8565-41-9 (ISBN)
Conference
2nd International Conference on Pattern Recognition Applications and Methods (ICPRAM), February 15-18, 2013, Barcelona, Spain
Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2017-02-08Bibliographically approved
5. Simulation of bright-field microscopy images depicting pap-smear specimen
Open this publication in new window or tab >>Simulation of bright-field microscopy images depicting pap-smear specimen
2015 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 87, no 3, 212-226 p.Article in journal (Refereed) Published
Abstract [en]

As digital imaging is becoming a fundamental part of medical and biomedical research, the demand for computer-based evaluation using advanced image analysis is becoming an integral part of many research projects. A common problem when developing new image analysis algorithms is the need of large datasets with ground truth on which the algorithms can be tested and optimized. Generating such datasets is often tedious and introduces subjectivity and interindividual and intraindividual variations. An alternative to manually created ground-truth data is to generate synthetic images where the ground truth is known. The challenge then is to make the images sufficiently similar to the real ones to be useful in algorithm development. One of the first and most widely studied medical image analysis tasks is to automate screening for cervical cancer through Pap-smear analysis. As part of an effort to develop a new generation cervical cancer screening system, we have developed a framework for the creation of realistic synthetic bright-field microscopy images that can be used for algorithm development and benchmarking. The resulting framework has been assessed through a visual evaluation by experts with extensive experience of Pap-smear images. The results show that images produced using our described methods are realistic enough to be mistaken for real microscopy images. The developed simulation framework is very flexible and can be modified to mimic many other types of bright-field microscopy images.

Keyword
Synthetic image generation, Pap-smear, brightfield microscopy
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-212514 (URN)10.1002/cyto.a.22624 (DOI)000349984200005 ()25573002 (PubMedID)
Available from: 2015-01-08 Created: 2013-12-11 Last updated: 2017-12-06Bibliographically approved
6. Multi-resolution Cervical Cell Dataset
Open this publication in new window or tab >>Multi-resolution Cervical Cell Dataset
2013 (English)Report (Other academic)
Place, publisher, year, edition, pages
Uppsala, Sweden: Centre for Image Analysis, Swedish University of Agricultural Sciences, 2013. 9 p.
Series
External report (Blue series), 37
Keyword
Pap smear, multi-resolution, cervical cancer
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-212505 (URN)
Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2014-01-24Bibliographically approved

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