Find in Library

A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both <i>tumor</i> and <i>non-tumor</i> areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled <i>tumor areas</i>. This is achieved by using <i>positive and unlabeled</i> learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.