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Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
Tallinn Univ Technol, TJ Seebeck Dept Elect, Tallinn, Estonia.ORCID-id: 0000-0003-3557-4947
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. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.ORCID-id: 0000-0001-7312-8222
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi. (Fredrik Pontén)ORCID-id: 0000-0003-2777-8114
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2017 (Engelska)Ingår i: Image Analysis: Part I, Springer, 2017, s. 407-418Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.

Ort, förlag, år, upplaga, sidor
Springer, 2017. s. 407-418
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10269
Nationell ämneskategori
Datorseende och robotik (autonoma system) Klinisk laboratoriemedicin
Forskningsämne
Datoriserad bildbehandling; Patologi
Identifikatorer
URN: urn:nbn:se:uu:diva-334218DOI: 10.1007/978-3-319-59126-1_34ISI: 000454359300034ISBN: 978-3-319-59125-4 (tryckt)OAI: oai:DiVA.org:uu-334218DiVA, id: diva2:1159082
Konferens
SCIA 2017, June 12–14, Tromsø, Norway
Forskningsfinansiär
Vinnova, 2016-02329Tillgänglig från: 2017-05-19 Skapad: 2017-11-21 Senast uppdaterad: 2020-01-08Bibliografiskt granskad
Ingår i avhandling
1. Methods for Processing and Analysis of Biomedical TEM Images
Öppna denna publikation i ny flik eller fönster >>Methods for Processing and Analysis of Biomedical TEM Images
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
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
Nyckelord
image analysis, image processing, deep learning, transmission electron microscopy, denoising, super-resolution reconstruction, registration, detection
Nationell ämneskategori
Annan elektroteknik och elektronik
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-381800 (URN)978-91-513-0653-7 (ISBN)
Disputation
2019-06-05, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-05-15 Skapad: 2019-04-17 Senast uppdaterad: 2019-06-18

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Gupta, AnindyaSuveer, AmitLindblad, JoakimDragomir, AncaSintorn, Ida-Maria

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Gupta, AnindyaSuveer, AmitLindblad, JoakimDragomir, AncaSintorn, Ida-MariaSladoje, Nataša
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Avdelningen för visuell information och interaktionBildanalys och människa-datorinteraktionKlinisk och experimentell patologi
Datorseende och robotik (autonoma system)Klinisk laboratoriemedicin

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