Background and objective: The volume of the intracerebral hemorrhage (ICH) obtained from CT scans is essential for quantification and treatment planning. However,a fast and accurate volume acquisition brings great challenges. On the one hand, it is both time consuming and operator dependent for manual segmentation, which is the gold standard for volume estimation. On the other hand, low contrast to normal tissues, irregular shapes and distributions of the hemorrhage make the existing automatic segmentation methods hard to achieve satisfactory performance. Method: To solve above problems, a CNN-based architecture is proposed in this work, consisting of a novel model, which is named as Ψ-Net and a multi-level training strategy. In the structure of Ψ-Net, a self-attention block and a contextual-attention block is designed to suppresses the irrelevant information and segment border areas of the hemorrhage more finely. Further, an multi-level training strategy is put forward to facilitate the training process. By adding the slice-level learning and a weighted loss, the multi-level training strategy effectively alleviates the problems of vanishing gradient and the class imbalance. The proposed training strategy could be applied to most of the segmentation networks, especially for complex models and on small datasets. Results: The proposed architecture is evaluated on a spontaneous ICH dataset and a traumatic ICH dataset. Compared to the previous works on the ICH sementation, the proposed architecture obtains the state-of-the-art performance(Dice of 0.950) on the spontaneous ICH, and comparable results(Dice of 0.895) with the best method on the traumatic ICH. On the other hand, the time consumption of the proposed architecture is much less than the previous methods on both training and inference. Morever, experiment results on various of models prove the universality of the multi-level training strategy. Conclusions: This study proposed a novel CNN-based architecture, Ψ-Net with multi-level training strategy. It takes less time for training and achives superior performance than previous ICH segmentaion methods.

背景和目的：从CT扫描获得的脑内出血（ICH）量对于量化和治疗计划至关重要。但是，快速准确的体积采集带来了巨大的挑战。一方面，手动分割既费时又取决于操作员，这是体积估计的黄金标准。另一方面，与正常组织的对比度低，出血的不规则形状和分布使得现有的自动分割方法难以实现令人满意的性能。方法：为解决上述问题，本文提出了一种基于CNN的体系结构，该体系结构由一个名为Ψ-Net的新颖模型和一种多级训练策略组成。在Ψ-Net的结构中 设计了一个自我注意块和一个上下文注意块，以更有效地抑制无关信息并细分出血的边界区域。此外，提出了多层次的培训策略以促进培训过程。通过增加片级学习和加权损失，多级训练策略有效地缓解了梯度消失和班级不平衡的问题。所提出的训练策略可以应用于大多数分割网络，尤其是对于复杂模型和小型数据集。结果：在自发性ICH数据集和创伤性ICH数据集上评估了提出的体系结构。与先前关于ICH划分的工作相比，该架构获得了自发ICH的最新性能（Dice为0.950），创伤性ICH的最佳方法，结果相当（骰子为0.895）。另一方面，在训练和推论上，所提出的体系结构的时间消耗远小于先前的方法。此外，各种模型的实验结果证明了多级训练策略的普遍性。结论：这项研究提出了一种新颖的基于CNN的架构Ψ-Net，具有多层训练策略。与以前的ICH细分方法相比，它花费的培训时间更少，并且性能更高。结论：这项研究提出了一种新颖的基于CNN的架构Ψ-Net，具有多层训练策略。与以前的ICH细分方法相比，它花费的培训时间更少，并且性能更高。结论：这项研究提出了一种新颖的基于CNN的架构Ψ-Net，具有多层训练策略。与以前的ICH细分方法相比，它花费的培训时间更少，并且性能更高。