Self and Mixed Supervision to Improve Training Labels for Multi-Class
  Medical Image Segmentation
Jianfei Liu Christopher Parnell Ronald M. Summers
Accurate training labels are a key component for multi-class medical image
segmentation. Their annotation is costly and time-consuming because it requires
domain expertise. This work aims to develop a dual-branch network and
automatically improve training labels for multi-class image segmentation.
Transfer learning is used to train the network and improve inaccurate weak
labels sequentially. The dual-branch network is first trained by weak labels
alone to initialize model parameters. After the network is stabilized, the
shared encoder is frozen, and strong and weak decoders are fine-tuned by strong
and weak labels together. The accuracy of weak labels is iteratively improved
in the fine-tuning process. The proposed method was applied to a three-class
segmentation of muscle, subcutaneous and visceral adipose tissue on abdominal
CT scans. Validation results on 11 patients showed that the accuracy of
training labels was statistically significantly improved, with the Dice
similarity coefficient of muscle, subcutaneous and visceral adipose tissue
increased from 74.2% to 91.5%, 91.2% to 95.6%, and 77.6% to 88.5%, respectively
(p<0.05). In comparison with our earlier method, the label accuracy was also
significantly improved (p<0.05). These experimental results suggested that the
combination of the dual-branch network and transfer learning is an efficient
means to improve training labels for multi-class segmentation.