  Purpose: Automating tasks such as lung tumor localization and segmentation in
radiological images can free valuable time for radiologists and other clinical
personnel. Convolutional neural networks may be suited for such tasks, but
require substantial amounts of labeled data to train. Obtaining labeled data is
a challenge, especially in the medical domain. Methods: This paper investigates
the use of a teacher-student design to utilize datasets with different types of
supervision to train an automatic model performing pulmonary tumor segmentation
on computed tomography images. The framework consists of two models: the
student that performs end-to-end automatic tumor segmentation and the teacher
that supplies the student additional pseudo-annotated data during training.
Results: Using only a small proportion of semantically labeled data and a large
number of bounding box annotated data, we achieved competitive performance
using a teacher-student design. Models trained on larger amounts of semantic
annotations did not perform better than those trained on teacher-annotated
data. Conclusions: Our results demonstrate the potential of utilizing
teacher-student designs to reduce the annotation load, as less supervised
annotation schemes may be performed, without any real degradation in
segmentation accuracy.
