UZH-Logo

Maintenance Infos

Fluorescence Image Segmentation by using Digitally Reconstructed Fluorescence Images


Blumer, Clemens; Vivien, Cyprien; Oertner, Thomas G; Vetter, Thomas (2011). Fluorescence Image Segmentation by using Digitally Reconstructed Fluorescence Images. In: Microscopic Image Analysis with Applications in Biology, Heidelberg, Germany, 2 September 2011 - 2 September 2011.

Abstract

In biological experiments fluorescence imaging is used to image living and stimulated neurons. But the analysis of fluorescence images is a difficult task. It is not possible to conclude the shape of an object from fluorescence images alone. Therefore, it is not feasible to get good manual segmented nor ground truth data from fluorescence images. Supervised learning approaches are not possible without training data. To overcome this issues we propose to synthesize fluorescence images and call them 'Digitally Reconstructed Fluorescence Images'(DRFI). We propose how DRFIs are computed with data from ’Serial Block-Face Scanning Electron Microscopy’ (SBFS-EM). As novelty, we use DRFIs to learn a distribution model of dendrite intensities and apply it to classify pixels into spine and non-spine pixels. By using DRFIs as test data we also have the ground truth of spine and non-spine pixels and can validate the results. With DRFIs supervised learning of fluorescence images is feasible.

In biological experiments fluorescence imaging is used to image living and stimulated neurons. But the analysis of fluorescence images is a difficult task. It is not possible to conclude the shape of an object from fluorescence images alone. Therefore, it is not feasible to get good manual segmented nor ground truth data from fluorescence images. Supervised learning approaches are not possible without training data. To overcome this issues we propose to synthesize fluorescence images and call them 'Digitally Reconstructed Fluorescence Images'(DRFI). We propose how DRFIs are computed with data from ’Serial Block-Face Scanning Electron Microscopy’ (SBFS-EM). As novelty, we use DRFIs to learn a distribution model of dendrite intensities and apply it to classify pixels into spine and non-spine pixels. By using DRFIs as test data we also have the ground truth of spine and non-spine pixels and can validate the results. With DRFIs supervised learning of fluorescence images is feasible.

Downloads

49 downloads since deposited on 22 Jun 2013
38 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Interdisciplinary PhD Projects
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:2 September 2011
Deposited On:22 Jun 2013 15:33
Last Modified:05 Apr 2016 16:49
Publisher:s.n.
Official URL:http://www.miaab.org/miaab-2011-heidelberg-proceedings.html
Permanent URL: https://doi.org/10.5167/uzh-78711

Download

[img]
Preview
Filetype: PDF
Size: 2MB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations