Abstract
We describe and evaluate a method for segmenting cell nuclei from serial block-face scanning electron microscope volumes. The nucleus is a roughly ellipsoidal structure near the centre of each cell, appearing as an irregular ellipse in each image slice. It is common to segment it manually, which is very time-consuming. We use a Convolutional Neural Network to locate the boundary of the nuclei in each image slice. Geometric constraints are used to discard false matches. The full 3D shape of each nucleus is reconstructed by linking the boundaries in neighbouring slices. We demonstrate and evaluate the system on several large image volumes.
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Almutairi, Y., Cootes, T., Kadler, K. (2018). Segmentating Nucleus Membranes in SBFSEM Volume Data with Deep Neural Networks. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_3
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DOI: https://doi.org/10.1007/978-3-319-95921-4_3
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