Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal
  Segmentation
Matin Hosseinzadeh Anindo Saha Joeran Bosma Henkjan Huisman
Quality of deep convolutional neural network predictions strongly depends on
the size of the training dataset and the quality of the annotations. Creating
annotations, especially for 3D medical image segmentation, is time-consuming
and requires expert knowledge. We propose a novel semi-supervised learning
(SSL) approach that requires only a relatively small number of annotations
while being able to use the remaining unlabeled data to improve model
performance. Our method uses a pseudo-labeling technique that employs recent
deep learning uncertainty estimation models. By using the estimated
uncertainty, we were able to rank pseudo-labels and automatically select the
best pseudo-annotations generated by the supervised model. We applied this to
prostate zonal segmentation in T2-weighted MRI scans. Our proposed model
outperformed the semi-supervised model in experiments with the ProstateX
dataset and an external test set, by leveraging only a subset of unlabeled data
rather than the full collection of 4953 cases, our proposed model demonstrated
improved performance. The segmentation dice similarity coefficient in the
transition zone and peripheral zone increased from 0.835 and 0.727 to 0.852 and
0.751, respectively, for fully supervised model and the uncertainty-aware
semi-supervised learning model (USSL). Our USSL model demonstrates the
potential to allow deep learning models to be trained on large datasets without
requiring full annotation. Our code is available at
https://github.com/DIAGNijmegen/prostateMR-USSL.