论文标题
k-strip：一种新颖的分割算法，用于应用颅骨剥离的k空间
k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
论文作者
论文摘要
目标：提出一种新型的基于深度学习的颅骨剥离算法，用于直接在信息丰富的K空间中起作用的磁共振成像（MRI）。 材料和方法：使用来自总共36,900个MRI切片的不同机构的两个数据集，我们训练了一个基于深度学习的模型，直接与复杂的原始K空间数据合作。图像结构域中由HD-BET（大脑提取工具）执行的头骨剥离被用作地面真相。 结果：两个数据集都与地面真相非常相似（骰子得分为92 \％-98 \％，而Hausdorff距离为5.5 mm以下）。在眼睛区域上方的切片上的结果达到99 \％，而眼睛周围及以下区域的精度下降，输出部分模糊。 K-strip的输出通常在对头骨的分界线时平滑边缘。用适当的阈值创建二进制面具。 结论：通过这项概念验证研究，我们能够显示在K空间频域中工作，保留相信息的可行性，并保持一致的结果。未来的研究应致力于发现K空间可用于创新图像分析和进一步工作流程的其他方式。
Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work directly with the complex raw k-space data. Skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain were used as the ground truth. Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above the eye-region reach DICE scores of up to 99\%, while the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-strip often smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. Conclusion: With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.
