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Fast Adaptive Local Thresholding Based on Ellipse fit
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Quantitative Microscopy)ORCID iD: 0000-0002-6699-4015
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Quantitative Microscopy)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Quantitative Microscopy)
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose an adaptive thresholding method where each object is thresholded optimizing its shape. The method is based on a component tree representation, which can be computed in quasi-linear time. We test and evaluate the method on images of bacteria from three different live-cell analysis experiments and show that the proposed method produces segmentation results comparable to state-of-the-art but at least an order of magnitude faster. The method can be extended to compute any feature measurements that can be calculated in a cumulative way, and holds great potential for applications where a priori information on expected object size and shape is available.

Place, publisher, year, edition, pages
2016.
Series
Scripta minora Bibliothecae regiae Universitatis Upsaliensis, ISSN 0282-3152
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-275182ISI: 000386377400049ISBN: 9781479923496 (print)ISBN: 9781479923502 (print)OAI: oai:DiVA.org:uu-275182DiVA: diva2:899148
Conference
International Symposium on Biomedical Imaging (ISBI'16), Prague, Czech Republic, April 13-16, 2016
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish Research Council, 2012-4968
Available from: 2016-02-01 Created: 2016-02-01 Last updated: 2017-09-21Bibliographically approved
In thesis
1. Deep Neural Networks and Image Analysis for Quantitative Microscopy
Open this publication in new window or tab >>Deep Neural Networks and Image Analysis for Quantitative Microscopy
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Understanding biology paves the way for discovering drugs targeting deadly diseases like cancer, and microscopy imaging is one of the most informative ways to study biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. Automated approaches for analysis of microscopy image data makes it possible to handle large data sets, and at the same time reduce the risk of bias. Quantitative microscopy refers to computational methods for extracting measurements from microscopy images, enabling detection and comparison of subtle changes in morphology or behavior induced by varying experimental conditions. This thesis covers computational methods for segmentation and classification of biological samples imaged by microscopy.

Recent increase in computational power has enabled the development of deep neural networks (DNNs) that perform well in solving real world problems. This thesis compares classical image analysis algorithms for segmentation of bacteria cells and introduces a novel method that combines classical image analysis and DNNs for improved cell segmentation and detection of rare phenotypes. This thesis also demonstrates a novel DNN for segmentation of clusters of cells (spheroid), with varying sizes, shapes and textures imaged by phase contrast microscopy. DNNs typically require large amounts of training data. This problem is addressed by proposing an automated approach for creating ground truths by utilizing multiple imaging modalities and classical image analysis. The resulting DNNs are applied to segment unstained cells from bright field microscopy images. In DNNs, it is often difficult to understand what image features have the largest influence on the final classification results. This is addressed in an experiment where DNNs are applied to classify zebrafish embryos based on phenotypic changes induced by drug treatment. The response of the trained DNN is tested by ablation studies, which revealed that the networks do not necessarily learn the features most obvious at visual examination. Finally, DNNs are explored for classification of cervical and oral cell samples collected for cancer screening. Initial results show that the DNNs can respond to very subtle malignancy associated changes. All the presented methods are developed using open-source tools and validated on real microscopy images.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 85 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1566
Keyword
Deep neural networks, convolutional neural networks, image analysis, quantitative microscopy, bright-field microscopy
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329834 (URN)978-91-513-0080-1 (ISBN)
Public defence
2017-11-10, 2446, ITC, Lägerhyddsvägen 2, Hus 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 682810eSSENCE - An eScience Collaboration
Available from: 2017-10-17 Created: 2017-09-21 Last updated: 2017-10-17

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Ranefall, PetterSadanandan, Sajith KecherilWählby, Carolina

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