  In this work, we improve the performance of multi-atlas segmentation (MAS) by
integrating the recently proposed VoteNet model with the joint label fusion
(JLF) approach. Specifically, we first illustrate that using a deep
convolutional neural network to predict atlas probabilities can better
distinguish correct atlas labels from incorrect ones than relying on image
intensity difference as is typical in JLF. Motivated by this finding, we
propose VoteNet+, an improved deep network to locally predict the probability
of 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 fusion
method than plurality voting. Lastly, we use Platt scaling to calibrate the
probabilities of our new model. Results on LPBA40 3D MR brain images show that
our proposed method can achieve better performance than VoteNet.
