Automated multi-organ segmentation on abdominal CT images may replace or complement manual segmentation for clinical applications including image-guided radiation therapy. However, the accuracy of auto-segmentation is challenged by low image contrast, large spatial and inter-patient anatomical variations. In this study, we propose an end-to-end segmentation network, termed self-paced DenseNet, for improved multi-organ segmentation performance, especially for the difficult-to-segment organs. Specifically, a learning-based attention mechanism and dense connection block are seamlessly integrated into the proposed self-paced DenseNet to improve the learning capability and efficiency of the backbone network. To heavily focus on the organs showing low soft-tissue contrast and motion artifacts, a boundary condition is utilized to constrain the network optimization. Additionally, to ease the large learning pace discrepancies of individual organs, a task-wise self-paced-learning strategy...

中文翻译：

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具有边界约束的自定进度DenseNet，可在腹部CT图像上自动进行多器官分割。

腹部CT图像上的自动多器官分割可以替代或补充手动分割，以用于包括图像引导放射治疗在内的临床应用。然而，自动分割的准确性受到低图像对比度，大空间和患者间解剖变化的挑战。在这项研究中，我们提出了一个端到端的分割网络，称为自定步距的DenseNet，以改善多器官分割的性能，尤其是对于难以分割的器官。具体而言，将基于学习的注意力机制和密集连接块无缝集成到所提出的自定进度的DenseNet中，以提高骨干网的学习能力和效率。要专注于显示低软组织对比度和运动伪影的器官，利用边界条件来约束网络优化。此外，为了缓解各个器官的较大学习速度差异，采用任务型自定进度的学习策略...