Deep Reinforcement Learning for Brain Aneurysm Segmentation in 3D TOF MRA Images: A Comparative Study using 3D U-Net, 3D ResNet, and LSTM Networks

Brain aneurysms pose a significant threat to human health, necessitating early and accurate detection for effective treatment. This study addresses the critical task of aneurysm segmentation in 3D Time-of-Flight Magnetic Resonance Angiography (3D TOF MRA) images using deep reinforcement learning (DRL). We explore the use of three distinct neural network architectures, namely 3D U-Net, 3D ResNet, and LSTM networks, within our DRL framework. The results indicate the effectiveness of the proposed approach. The 3D U-Net demonstrates a respectable Intersection over Union (IoU) of 0.84, showcasing its ability to accurately identify aneurysms in the complex vascular structures. The 3D ResNet further improves the IoU to 0.88, capitalizing on its deep architecture with skip connections. However, the LSTM network outshines both with an impressive IoU of 0.92, highlighting its efficacy in navigating through the 3D image data to discover aneurysms efficiently. This research signifies a significant advancement in the field of medical image analysis, emphasizing the potential of deep reinforcement learning in aneurysm segmentation. The promising results obtained from the LSTM network offer a pathway for accurate and swift aneurysm detection, reducing the time and effort required for clinical evaluation. Furthermore, the study underscores the importance of selecting an appropriate neural network backbone for medical image segmentation and the synergy achieved through the combination of 3D U-Net, 3D ResNet, and LSTM networks. The implications of this work are not limited to aneurysm detection but extend to a broader spectrum of medical imaging tasks, demonstrating the promise of DRL as a potent tool in the arsenal of medical professionals. These findings offer a basis for future research, emphasizing the need for more extensive clinical validation and broader adoption of deep reinforcement learning techniques in the medical field.