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.

Self-supervised Anatomical eMbedding (SAM) is introduced to extract semantic embeddings for imaging pixels with intrinsic structures, which can be used to locate body parts in other images by nearest neighbor searching, surpassing supervised methods trained on 50 labeled images with only one labeled template image and being effective in improving image registration and initializing CNN weights.

自监督学习放射学图像像素级解剖嵌入

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

Fast nearest neighbor searching is becoming an increasingly important tool in
solving many large-scale problems. Recently a number of approaches to learning
data-dependent hash functions have been developed. In this work, we propose a
column generation based method for learning data-dependent hash functions on
the basis of proximity comparison information. Given a set of triplets that
encode the pairwise proximity comparison information, our method learns hash
functions that preserve the relative comparison relationships in the data as
well as possible within the large-margin learning framework. The learning
procedure is implemented using column generation and hence is named CGHash. At
each iteration of the column generation procedure, the best hash function is
selected. Unlike most other hashing methods, our method generalizes to new data
points naturally; and has a training objective which is convex, thus ensuring
that the global optimum can be identified. Experiments demonstrate that the
proposed method learns compact binary codes and that its retrieval performance
compares favorably with state-of-the-art methods when tested on a few benchmark
datasets.

提出了基于列生成的学习数据相关哈希函数的建议，通过三元组来学习哈希函数，使用大边缘学习框架尽可能地保留数据的相对比较关系。实验表明，该方法学习到了紧凑的二进制代码，并且当在一些基准数据集上进行测试时，其检索性能与最先进的方法相比具有优势。