  Automatic segmentation of anatomical structures with convolutional neural
networks (CNNs) constitutes a large portion of research in medical image
analysis. The majority of CNN-based methods rely on an abundance of labeled
data for proper training. Labeled medical data is often scarce, but unlabeled
data is more widely available. This necessitates approaches that go beyond
traditional supervised learning and leverage unlabeled data for segmentation
tasks. This work investigates the potential of autoencoder-extracted features
to improve segmentation with a CNN. Two strategies were considered. First,
transfer learning where pretrained autoencoder features were used as
initialization for the convolutional layers in the segmentation network.
Second, multi-task learning where the tasks of segmentation and feature
extraction, by means of input reconstruction, were learned and optimized
simultaneously. A convolutional autoencoder was used to extract features from
unlabeled data and a multi-scale, fully convolutional CNN was used to perform
the target task of 3D liver segmentation in CT images. For both strategies,
experiments were conducted with varying amounts of labeled and unlabeled
training data. The proposed learning strategies improved results in $75\%$ of
the experiments compared to training from scratch and increased the dice score
by up to $0.040$ and $0.024$ for a ratio of unlabeled to labeled training data
of about $32 : 1$ and $12.5 : 1$, respectively. The results indicate that both
training strategies are more effective with a large ratio of unlabeled to
labeled training data.
