We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891±0.016 (mean±std) and an average symmetric surface distance (ASD) of 0.994±0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845±0.031 DC and 1.556±0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in activelearning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
In this paper, we propose a real-time visualization system utilizing Augmented Reality Technology for electromagnetics education. It gives an image of magnetic field generated by a bar magnet with a piece of iron in real-time, however the bar magnet and the piece of iron are represented by mock ones. In the newly proposed visualization system, these mocks are captured by a web camera, and mesh needed in the calculation of magnetic field is deformed. Subsequently, a finite element analysis is carried out in very short time and then the magnetic field is immediately visualized. Thereby, it is, in real-time, observable that magnetic flux lines generated by the bar magnet are attracted to a piece of iron. Moreover, when a user moves the mocks, the magnetic flux lines are immediately depicted according to the position of the mocks.
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