Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods and can be crucial for downstream radiation treatment planning. U-net has become a de-facto standard for medical image segmentation and is frequently used as a common baseline in medical image segmentation tasks. In this paper, we propose a multiple decoder U-net architecture and use the segmentation disagreement between the decoders as attention to the bottleneck of the network for segmentation refinement. While feature correlation is considered as attention in most cases, in our case it is the uncertainty from the network used as attention. For accurate segmentation, we also proposed a CT intensity integrated regularization loss. Proposed regularisation helps model understand the intensity distribution of low contrast tissues. We tested our model on two publicly available OAR challenge datasets. We also conducted the ablation on each datasets with the proposed attention module and regularization loss. Experimental results demonstrate a clear accuracy improvement on both datasets.

翻译：在计算机断层扫描（CT）图像中，危及器官（OAR）分割是一项具有挑战性的自动分割任务，且对后续放射治疗计划至关重要。U-net已成为医学图像分割的事实标准，并常被用作医学图像分割任务中的通用基准。本文提出了一种多解码器U-net架构，利用解码器之间的分割不一致性作为网络瓶颈的注意力机制，以优化分割结果。在多数情况下，特征相关性被视为注意力，而本文则使用网络产生的不确定性作为注意力。为实现精确分割，我们还提出了一种CT强度积分正则化损失函数。所提出的正则化方法有助于模型理解低对比度组织的强度分布。我们在两个公开的OAR挑战数据集上测试了模型，并在每个数据集上对提出的注意力模块和正则化损失进行了消融实验。实验结果表明，两个数据集上的分割精度均得到显著提升。