arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.02383cs.CV

SAM：放射学图像中像素级解剖嵌入的自监督学习

SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

Alibaba Group DAMO Academy（阿里巴巴集团达摩院）；PAII Inc.（PAII公司）；Ping An Insurance (Group) Company of China, Ltd.（中国平安保险（集团）股份有限公司）；Beijing United Family Hospital（北京和睦家医院）

Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Dazhou Guo, Adam P. Harrison, Youbao Tang, Jing Xiao, Jingjing Lu, Le Lu

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AI总结：

提出自监督解剖嵌入方法SAM，通过像素级对比学习和由粗到细策略学习放射学图像的内在解剖结构，仅需少量或无标注即可在2D/3D图像中实现快速、高精度的解剖定位，并可用于病灶匹配、配准改进及CNN权重初始化。

摘要：

诸如计算机断层扫描（CT）和X射线等放射学图像以内在结构呈现解剖结构。能够在不同图像中可靠地定位同一解剖结构是医学图像分析中的一项基本任务。原则上，可以使用关键点检测或语义分割来完成此任务，但要使其良好工作，需要为每个感兴趣的解剖结构和子结构提供大量标注数据。一种更通用的方法是从无标签图像中学习内在结构。我们引入了这样一种方法，称为自监督解剖嵌入（SAM）。SAM为每个图像像素生成语义嵌入，描述其解剖位置或身体部位。为了生成此类嵌入，我们提出了一种像素级对比学习框架。由粗到细的策略确保了全局和局部解剖信息均被编码。负样本选择策略旨在增强嵌入的判别能力。使用SAM，可以在模板图像上为任意感兴趣点进行标注，然后通过简单的最近邻搜索在其他图像中定位相同的身体部位。我们在2D和3D图像模态的多个任务中展示了SAM的有效性。在一个包含19个关键点的胸部CT数据集上，SAM优于广泛使用的配准算法，同时推理仅需0.23秒。在两个X射线数据集上，SAM仅使用一张标注模板图像，就超越了在50张标注图像上训练的监督方法。我们还将SAM应用于CT全身随访病灶匹配，并获得了91%的准确率。SAM还可用于改进图像配准和初始化CNN权重。

英文摘要：

Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying images is a fundamental task in medical image analysis. In principle it is possible to use landmark detection or semantic segmentation for this task, but to work well these require large numbers of labeled data for each anatomical structure and sub-structure of interest. A more universal approach would learn the intrinsic structure from unlabeled images. We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM). SAM generates semantic embeddings for each image pixel that describes its anatomical location or body part. To produce such embeddings, we propose a pixel-level contrastive learning framework. A coarse-to-fine strategy ensures both global and local anatomical information are encoded. Negative sample selection strategies are designed to enhance the embedding's discriminability. Using SAM, one can label any point of interest on a template image and then locate the same body part in other images by simple nearest neighbor searching. We demonstrate the effectiveness of SAM in multiple tasks with 2D and 3D image modalities. On a chest CT dataset with 19 landmarks, SAM outperforms widely-used registration algorithms while only taking 0.23 seconds for inference. On two X-ray datasets, SAM, with only one labeled template image, surpasses supervised methods trained on 50 labeled images. We also apply SAM on whole-body follow-up lesion matching in CT and obtain an accuracy of 91%. SAM can also be applied for improving image registration and initializing CNN weights.

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