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Automated detection of cilia in low magnification transmission electron microscopy images using template matching
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. (Centre for Image Analysis)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia. (Centre for Image Analysis)
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. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia. (Centre for Image Analysis)ORCID iD: 0000-0001-7312-8222
Uppsala University Hospital.
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2016 (English)In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, 2016, p. 386-390Conference paper, Published paper (Other academic)
Abstract [en]

Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.

Place, publisher, year, edition, pages
IEEE, 2016. p. 386-390
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords [en]
Image resolution, Transmission Electron Microscopy, Object detection, Shape, Image analysis, Template matching
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-308090DOI: 10.1109/ISBI.2016.7493289ISI: 000386377400093ISBN: 9781479923496 (print)ISBN: 9781479923502 (print)OAI: oai:DiVA.org:uu-308090DiVA, id: diva2:1049181
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2019-04-17Bibliographically approved
In thesis
1. Methods for Processing and Analysis of Biomedical TEM Images
Open this publication in new window or tab >>Methods for Processing and Analysis of Biomedical TEM Images
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Transmission Electron Microscopy (TEM) has the high resolving capability and high clinical significance; however, the current manual diagnostic procedure using TEM is complicated and time-consuming, requiring rarely available expertise for analyzing TEM images of the biological specimen. This thesis addresses the bottlenecks of TEM-based analysis by proposing image analysis methods to automate and improve critical time-consuming steps of currently manual diagnostic procedures. The automation is demonstrated on the computer-assisted diagnosis of Primary Ciliary Dyskinesia (PCD), a genetic condition for which TEM analysis is considered the gold standard.

The methods proposed for the automated workflow mimic the manual procedure performed by the pathologists to detect objects of interest – diagnostically relevant cilia instances – followed by a computational step to combine information from multiple detected objects to enhance the important structural details. The workflow includes an approach for efficient search through a sample to identify objects and locate areas with a high density of objects of interest in low-resolution images, to perform high-resolution imaging of the identified areas. Subsequently, high-quality objects in high-resolution images are detected, processed, and the extracted information is combined to enhance structural details.

This thesis also addresses the challenges typical for TEM imaging, such as sample drift and deformation, or damage due to high electron dose for long exposure times. Two alternative paths are investigated: (i) different strategies combining short exposure imaging with suitable denoising techniques, including conventional approaches and a proposed deep learning based method, are explored; (ii) conventional interpolation approaches and a proposed deep learning based method are analyzed for super-resolution reconstruction using a single image. For both explored directions, in the best case scenario, the processing time is nearly 20 times faster as compared to the acquisition time for a single long exposure high illumination image. Moreover, the reconstruction approach (ii) requires nearly 16 times lesser data (storage space) and overcomes the need for high-resolution image acquisition.

Finally, the thesis addresses critical needs to enable objective and reliable evaluation of TEM image denoising approaches. A method for synthesizing realistic noise-free TEM reference images is proposed, and a denoising benchmark dataset is generated and made publicly available. The proposed dataset consists of noise-free references along with masks encompassing the critical diagnostic structures. This enables performance evaluation based on the capability of denoising methods to preserve structural details, instead of merely grading them based on the signal to noise ratio improvement and preservation of gross structures.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 49
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1807
Keywords
image analysis, image processing, deep learning, transmission electron microscopy, denoising, super-resolution reconstruction, registration, detection
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-381800 (URN)978-91-513-0653-7 (ISBN)
Public defence
2019-06-05, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2019-05-15 Created: 2019-04-17 Last updated: 2019-06-18

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Suveer, AmitSladoje, NatašaLindblad, JoakimDragomir, AncaSintorn, Ida-Maria

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Computerized Image Analysis and Human-Computer InteractionDivision of Visual Information and Interaction
Computer Vision and Robotics (Autonomous Systems)

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