Zusammenfassung
Whole brain segmentation from structural MRI-T1 scan is a prerequisite for most morphological analyses, but requires hours of processing time and therefore delays the availability of image markers after scan acquisition. We introduced a fully convolution neural network (F-CNN) that segments a brain scan in several seconds [1]. Training deep F-CNNs for semantic image segmentation requires access to abundant labeled data.
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Literatur
Error corrective boosting for learning fully convolutional networks with limited data. Springer 2017.
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. In Neuron; 2002.
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Roy, A.G., Conjeti, S., Navab, N., Wachinger, C. (2018). Abstract: Fast MRI Whole Brain Segmentation with Fully Convolutional Neural Networks. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_26
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DOI: https://doi.org/10.1007/978-3-662-56537-7_26
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