论文标题
基于深度学习的各种大脑病变的分割用于放射外科手术
Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery
论文作者
论文摘要
具有深度学习模型的医学图像的语义分割迅速开发。在这项研究中，我们在临床立体定向放射外科数据集上基于最新的深度学习分割算法，证明了这些算法在相当实用的情况下的优势和劣势。特别是，我们比较了模型性能相对于其采样方法，模型体系结构以及损失功能的选择，确定适合其应用程序的设置，并阐明可能的改进。
Semantic segmentation of medical images with deep learning models is rapidly developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset, demonstrating the strengths and weaknesses of these algorithms in a fairly practical scenario. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions, identifying the suitable settings for their applications and shedding light on the possible improvements.
