No 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 Unet 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.

Purpose Image classification is perhaps the most fundamental task in imaging artificial intelligence. However, labeling images is time-consuming and tedious. We have recently demonstrated that reinforcement learning (RL) can classify 2D slices of MRI brain images with high accuracy.Here we make two important steps toward speeding image classification: Firstly, we automatically extract class labels from the clinical reports. Secondly, we extend our prior 2D classification work to fully 3D image volumes from our institution. Hence, we proceed as follows: in Part 1, we extract labels from reports automatically using a natural language processing approach termed sentence bidirectional encoder representations from transformers (SBERT). Then, in Part 2, we use these labels with RL to train a classification Deep-Q Network (DQN) for 3D image volumes.Materials and Methods For Part 1, we trained SBERT with 45 "normal" patient report impressions and 45 metastasis-containing impressions. We then used the trained SBERT to predict class labels for use in Part 2. In Part 2, we applied multi-step image classification to allow for combined Deep-Q learning using 3D convolutions and TD(0) Q learning. We trained on a set of 90 images (40 normal and 50 tumor-containing). We tested on a separate set of 61 images (40 normal and 21 tumor-containing), again using the classes predicted from patient reports by the trained SBERT in Part 1. For comparison, we also trained and tested a supervised deep learning classification network on the same set of training and testing images using the same labels.Results Part 1: Upon training with the corpus of radiology reports, the SBERT model had 100% accuracy for both normal and metastasiscontaining scans. Part 2: Then, using these labels, whereas the supervised

No abstract

No abstract

No abstract

No abstract

Background Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition. It has recently been proposed that reinforcement learning addresses all three of these limitations. Notable prior work applied deep reinforcement learning to localize brain tumors with radiologist eye tracking points, which limits the state-action space. Here, we generalize Deep Q Learning to a gridworld-based environment so that only the images and image masks are required. Methods We trained a Deep $$Q$$ Q network on 30 two-dimensional image slices from the BraTS brain tumor database. Each image contained one lesion. We then tested the trained Deep Q network on a separate set of 30 testing set images. For comparison, we also trained and tested a keypoint detection 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 (11% accuracy), the Deep $$Q$$ Q learning approach showed progressive improved generalizability to the testing set over training time, reaching 70% accuracy. Conclusion We have successfully applied reinforcement learning to localize brain tumors on 2D contrast-enhanced MRI brain images. This represents a generalization of recent work to a gridworld setting naturally suitable for analyzing medical images. We have shown that reinforcement learning does not over-fit small training sets, and can generalize to a separate testing set.