自监督生成医学图像体素的解剖位置嵌入，提升器官定位精度。
Anatomical Positional Embeddings
- 基于自监督学习构建体素级解剖位置嵌入，编码器官间空间关系。
- 在13个腹部器官定位任务中表现优于现有方法，召回率达0.99。
- 可低成本实现器官区域自动裁剪，体积减少10-100倍，适合临床应用。
我们提出一种自监督模型，用于生成医学图像体素的3D解剖位置嵌入（APE）。APE编码体素间的解剖相近性，即同一器官或邻近器官的体素位置嵌入更接近，而远距离器官的体素嵌入则较远。与现有解剖位置嵌入模型相比，本方法能高效生成整个体积输入图像的体素级嵌入图，适用于多种下游任务。我们在8400张腹部和胸部区域的公开CT图像上训练APE模型，并在解剖标志物检索与13个腹部器官的弱监督少样本定位任务中证明其性能优越。作为实际应用，我们展示了如何低成本训练APE模型，以0.99的召回率将原始CT图像裁剪至不同解剖兴趣区域，同时将图像体积减少10-100倍。代码与预训练的APE模型可在https://github.com/mishgon/ape 获取。
原文摘要 · Abstract (English)
We propose a self-supervised model producing 3D anatomical positional embeddings (APE) of individual medical image voxels. APE encodes voxels' anatomical closeness, i.e., voxels of the same organ or nearby organs always have closer positional embeddings than the voxels of more distant body parts. In contrast to the existing models of anatomical positional embeddings, our method is able to efficiently produce a map of voxel-wise embeddings for a whole volumetric input image, which makes it an optimal choice for different downstream applications. We train our APE model on 8400 publicly available CT images of abdomen and chest regions. We demonstrate its superior performance compared with the existing models on anatomical landmark retrieval and weakly-supervised few-shot localization of 13 abdominal organs. As a practical application, we show how to cheaply train APE to crop raw CT images to different anatomical regions of interest with 0.99 recall, while reducing the image volume by 10-100 times. The code and the pre-trained APE model are available at https://github.com/mishgon/ape .
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