Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is
of great significance for radiotherapy planning. However, it is a challenging
task to learn powerful representations for accurate delineation under limited
pixel (voxel)-wise annotations. Contrastive learning at pixel-level can
alleviate the dependency on annotations by learning dense representations from
unlabeled data. Recent studies in this direction design various contrastive
losses on the feature maps, to yield discriminative features for each pixel in
the map. However, pixels in the same map inevitably share semantics to be
closer than they actually are, which may affect the discrimination of pixels in
the same map and lead to the unfair comparison to pixels in other maps. To
address these issues, we propose a separated region-level contrastive learning
scheme, namely SepaReg, the core of which is to separate each image into
regions and encode each region separately. Specifically, SepaReg comprises two
components: a structure-aware image separation (SIS) module and an intra- and
inter-organ distillation (IID) module. The SIS is proposed to operate on the
image set to rebuild a region set under the guidance of structural information.
The inter-organ representation will be learned from this set via typical
contrastive losses cross regions. On the other hand, the IID is proposed to
tackle the quantity imbalance in the region set as tiny organs may produce
fewer regions, by exploiting intra-organ representations. We conducted
extensive experiments to evaluate the proposed model on a public dataset and
two private datasets. The experimental results demonstrate the effectiveness of
the proposed model, consistently achieving better performance than
state-of-the-art approaches. Code is available at
this https URL

该研究提出了一种名为 SepaReg 的分离区域级对比学习方案，通过图像分离和内部 - 跨组织蒸馏，解决了有限的像素注释和像素的语义相似度问题，从而在被用于放射治疗计划中的器官以及肿瘤体积的识别和定位中实现了更准确的性能表现。