Votenet +: An Improved Deep Learning Label Fusion Method for Multi-Atlas Segmentation | IEEE Conference Publication | IEEE Xplore

Votenet +: An Improved Deep Learning Label Fusion Method for Multi-Atlas Segmentation


Abstract:

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. ...Show More

Abstract:

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 differ 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.
Date of Conference: 03-07 April 2020
Date Added to IEEE Xplore: 22 May 2020
ISBN Information:

ISSN Information:

PubMed ID: 35261721
Conference Location: Iowa City, IA, USA

References

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