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In this work, we improve the performance of multi-atlas segmentation (MAS) byintegrating the recently proposed VoteNet model with the joint label fusion(JLF) approach. Specifically, we first illustrate that using a deepconvolutional neural network to predict atlas probabilities can betterdistinguish correct atlas labels from incorrect ones than relying on imageintensity difference as is typical in JLF. Motivated by this finding, wepropose VoteNet+, an improved deep network to locally predict the probabilityof an atlas label to differs from the label of the target image. Furthermore,we show that JLF is more suitable for the VoteNet framework as a label fusionmethod than plurality voting. Lastly, we use Platt scaling to calibrate theprobabilities of our new model. Results on LPBA40 3D MR brain images show thatour proposed method can achieve better performance than VoteNet.

VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation