Abstract
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that postprocessing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-the-art in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4\(\times \) faster and runs with 6\(\times \) less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.
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Notes
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Using the SciPy 0.17.0 implementation with default parameters https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html.
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Lieman-Sifry, J., Le, M., Lau, F., Sall, S., Golden, D. (2017). FastVentricle: Cardiac Segmentation with ENet. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_13
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