PDF(Pubmed)
Abstract：
Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications.
摘要：
传统上,从医学图像中构建自动肌肉分割的训练数据集，涉及熟练的操作员，导致较高的劳动力成本和有限的可扩展性。为了解决这个问题，我们开发了一种工具，可以让非专家进行有效的注释，并评估其训练自动分割网络的有效性。我们的系统允许用户对模板三维（3D）解剖模型进行变形，以使用具有轴向独立控制点的自由变形来拟合目标磁共振图像。矢状,和日冕方向。这种方法通过允许非专家直观地调整模型来简化注释过程，启用模板中所有肌肉的同时注释。我们评估了由非专家执行的工具辅助分割的质量，与专家分割相比，Dice系数大于0.75，没有明显的错误，例如错误标记相邻肌肉或省略肌肉组织。使用此工具创建的数据集训练的自动分割网络显示出与使用专家生成的数据集训练的网络相当或优于的性能。这种创新的工具大大减少了与自动肌肉分割数据集创建相关的时间和劳动力成本，潜在的革命性的医学图像标注和加速在各种临床应用中基于深度学习的分割网络的发展。
