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Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.ORCID-id: 0000-0003-3557-4947
Vise andre og tillknytning
2018 (engelsk)Inngår i: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, s. 921-925Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
IEEE, 2018. s. 921-925
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-367040DOI: 10.1109/ISBI.2018.8363721ISI: 000455045600210ISBN: 978-1-5386-3636-7 (digital)OAI: oai:DiVA.org:uu-367040DiVA, id: diva2:1266270
Konferanse
ISBI 2018, April 4–7, Washington, DC
Tilgjengelig fra: 2018-11-27 Laget: 2018-11-27 Sist oppdatert: 2019-04-17bibliografisk kontrollert
Inngår i avhandling
1. Methods for Processing and Analysis of Biomedical TEM Images
Åpne denne publikasjonen i ny fane eller vindu >>Methods for Processing and Analysis of Biomedical TEM Images
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2019. s. 49
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1807
Emneord
image analysis, image processing, deep learning, transmission electron microscopy, denoising, super-resolution reconstruction, registration, detection
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-381800 (URN)978-91-513-0653-7 (ISBN)
Disputas
2019-06-05, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-05-15 Laget: 2019-04-17 Sist oppdatert: 2019-06-18

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