Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets

TL;DR

This paper presents a novel approach combining unsupervised deep clustering with reinforcement learning to accurately segment brain tumors in MRI images using very small training sets, outperforming traditional supervised methods.

Contribution

It introduces a new method that leverages unsupervised clustering and reinforcement learning for lesion segmentation, reducing the need for large annotated datasets.

Findings

Unsupervised clustering and reinforcement learning achieved an 83% Dice score.

Supervised U-net overfitted with only 16% Dice score on test data.

The method requires minimal radiologist input and no hand-traced annotations.

Abstract

Purpose: Lesion segmentation in medical imaging is key to evaluating treatment response. We have recently shown that reinforcement learning can be applied to radiological images for lesion localization. Furthermore, we demonstrated that reinforcement learning addresses important limitations of supervised deep learning; namely, it can eliminate the requirement for large amounts of annotated training data and can provide valuable intuition lacking in supervised approaches. However, we did not address the fundamental task of lesion/structure-of-interest segmentation. Here we introduce a method combining unsupervised deep learning clustering with reinforcement learning to segment brain lesions on MRI. Materials and Methods: We initially clustered images using unsupervised deep learning clustering to generate candidate lesion masks for each MRI image. The user then selected the best mask for each of 10 training images. We then trained a reinforcement learning algorithm to select the masks. We tested the corresponding trained deep Q network on a separate testing set of 10 images. For comparison, we also trained and tested a U-net supervised deep learning network on the same set of training/testing images. Results: Whereas the supervised approach quickly overfit the training data and predictably performed poorly on the testing set (16% average Dice score), the unsupervised deep clustering and reinforcement learning achieved an average Dice score of 83%. Conclusion: We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation.