Multi-class image segmentation in fluorescence microscopy using polytrees

Fehri, H., Gooya, A. , Johnston, S.A. and Frangi, A.F. (2017) Multi-class image segmentation in fluorescence microscopy using polytrees. In: Niethammer, M., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T. and Shen, D. (eds.) Information Processing in Medical Imaging. Series: Lecture Notes in Computer Science, 10265. Springer: Cham, pp. 517-528. ISBN 9783319590493 (doi: 10.1007/978-3-319-59050-9_41)

Abstract

Multi-class segmentation is a crucial step in cell image analysis. This process becomes challenging when little information is available for recognising cells from the background, due to their poor discriminative features. To alleviate this, directed acyclic graphs such as trees have been proposed to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, modelling the relations between labels of multiple classes becomes difficult. To overcome this limitation, we propose a polytree graphical model that captures label proximity relations more naturally compared to tree based approaches. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on the polytree. The algorithm is evaluated using simulated data, synthetic images and real fluorescence microscopy images. Our method achieves Dice scores of 94.5% and 98% on macrophage and seed classes, respectively, outperforming GMM based classifiers.

• Item Type: Book Sections
• Additional Information: eISBN: 9783319590509.
• Status: Published
• Glasgow Author(s) Enlighten ID: Gooya, Dr Ali
• Authors: Fehri, H., Gooya, A., Johnston, S.A., and Frangi, A.F.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783319590493

Related URLs

• University of Glasgow Library Catalogue