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

12/24/2020
by   Joseph Stember, et al.
93

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 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 10

page 11

page 12

page 13

page 14

page 15

page 18

10/21/2020

Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets

Purpose Supervised deep learning in radiology suffers from notorious inh...
06/17/2021

Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes

Purpose: Image classification is perhaps the most fundamental task in im...
11/23/2018

Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder

Lesions that appear hyperintense in both Fluid Attenuated Inversion Reco...
01/26/2020

Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives

Deep learning has proven to be an essential tool for medical image analy...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.