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Minimal Annotation Training for Segmentation of Microscopy Images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-6148-5174
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: /0000-0002-8307-7411
2018 (English)In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, p. 387-390Conference paper, Published paper (Refereed)
Abstract [en]

In many biomedical applications, successful training of Convolutional Neural Networks (CNNs) is restricted by an insufficient amount of annotated images. Although image augmentation can help training CNNs from a relatively small image set, in many applications, the objects of interest cannot be accurately delineated due to their fuzzy shape, image quality or a limitation in time, experience or knowledge of the expert performing the annotation. We propose an approach for training a CNN for segmentation of images with minimal annotation. The annotation consists of center points or lines of target objects of approximately known size. We demonstrate this approach in the application of Rift Valley virus segmentation in a challenging transmission electron microscopy image dataset. Our method achieves a Dice score of 0.900 and intersection over union of 0.831. Using the suggested minimal annotation training is particularly useful for applications in which full object annotations are not available or feasible.

Place, publisher, year, edition, pages
IEEE, 2018. p. 387-390
Series
IEEE International Symposium on Biomedical Imaging, E-ISSN 1945-8452
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-368701DOI: 10.1109/ISBI.2018.8363599ISI: 000455045600088ISBN: 978-1-5386-3636-7 (electronic)OAI: oai:DiVA.org:uu-368701DiVA, id: diva2:1268701
Conference
15th IEEE International Symposium on Biomedical Imaging (ISBI),Washington, DC, April 04-07, 2018
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-02-01Bibliographically approved

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Matuszewski, Damian J.Sintorn, Ida-Maria

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