Abstract
Detection, segmentation, and quantification of individual cell nuclei is a standard task in biomedical applications. Due to the increasing volume of acquired image data, it is not possible to rely on manual labeling and object counting. Instead, automated image processing methods have to be applied. Especially in three-dimensional data, one of the major challenges is the separation of touching cell nuclei in densely packed clusters. In this paper, we propose a method for automated detection and segmentation of immunostained cell nuclei in ultramicroscopy images. Our algorithm utilizes interactive learning and voxel classification to obtain a foreground segmentation and subsequently performs the splitting process for each cluster using a multi-step watershed approach. We have evaluated our results using reference images manually labeled by domain experts and compare our approach to state-of-the art methods.
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Acknowledgments
This work has been partly supported by the Deutsche Forschungsgemeinschaft, CRC 656 “Cardiovascular Molecular Imaging”. The images in this paper have been rendered using the framework Voreen [13].
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Scherzinger, A., Kleene, F., Dierkes, C., Kiefer, F., Hinrichs, K.H., Jiang, X. (2016). Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_9
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DOI: https://doi.org/10.1007/978-3-319-45886-1_9
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