The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images.

翻译：机器学习在磁共振成像（MRI），特别是神经影像学中的整合已被证明极其有效，能够提高诊断准确性、加速图像分析并提供数据驱动的见解，从而可能改变患者护理模式。深度学习模型利用多层处理来捕捉复杂数据的精细细节，随后可应用于多种任务，包括脑肿瘤分类、分割、图像合成与配准。先前研究已证明，使用包括 nn-UNet 和 Swin-UNet 在内的多种模型架构进行肿瘤分割可获得高精度。采用状态空间建模的 U-Mamba 在医学图像分割中也实现了高精度。为充分利用这些模型，我们提出一种深度学习框架，集成这些先进架构以实现精确分割并生成精细的合成图像。