  Automated brain tumor segmentation based on deep learning (DL) has achieved
promising performance. However, it generally relies on annotated images for
model training, which is not always feasible in clinical settings. Therefore,
the development of unsupervised DL-based brain tumor segmentation approaches
without expert annotations is desired. Motivated by the success of prompt
learning (PL) in natural language processing, we propose an approach to
unsupervised brain tumor segmentation by designing image-based prompts that
allow indication of brain tumors, and this approach is dubbed as PL-based Brain
Tumor Segmentation (PL-BTS). Specifically, instead of directly training a model
for brain tumor segmentation with a large amount of annotated data, we seek to
train a model that can answer the question: is a voxel in the input image
associated with tumor-like hyper-/hypo-intensity? Such a model can be trained
by artificially generating tumor-like hyper-/hypo-intensity on images without
tumors with hand-crafted designs. Since the hand-crafted designs may be too
simplistic to represent all kinds of real tumors, the trained model may overfit
the simplistic hand-crafted task rather than actually answer the question of
abnormality. To address this problem, we propose the use of a validation task,
where we generate a different hand-crafted task to monitor overfitting. In
addition, we propose PL-BTS+ that further improves PL-BTS by exploiting
unannotated images with brain tumors. Compared with competing unsupervised
methods, the proposed method has achieved marked improvements on both public
and in-house datasets, and we have also demonstrated its possible extension to
other brain lesion segmentation tasks.
