West China School of Nursing, Sichuan University/ West China Hospital Critical Care Medicine Department, Sichuan University, Chengdu, 610041, China.
Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China.
Sci Rep. 2023 Mar 1;13(1):3440. doi: 10.1038/s41598-023-30581-4.
Intracranial hemorrhage is a cerebral vascular disease with high mortality. Automotive diagnosing and segmentation of intracranial hemorrhage in Computed Tomography (CT) could assist the neurosurgeon in making treatment plans, which improves the survival rate. In this paper, we design a grouped capsule network named GroupCapsNet to segment the hemorrhage region from a Non-contract CT scan. In grouped capsule network, we constrain the prediction capsules for output capsules produced from different groups of input capsules with various types in each layer. This method can reduce the number of intermediate prediction capsules and accelerate the capsule network. In addition, we modify the squashing function to further accelerate the forward procedure without sacrificing its performance. We evaluate our proposed method with a collected dataset containing 210 intracranial hemorrhage CT scan slices. In experiments, our proposed method achieves competitive results in intracranial hemorrhage area segmentation compared to the existing methods.
颅内出血是一种死亡率较高的脑血管疾病。在计算机断层扫描（CT）中对颅内出血进行自动诊断和分割，可以帮助神经外科医生制定治疗计划，从而提高生存率。在本文中，我们设计了一个名为 GroupCapsNet 的分组胶囊网络，用于从非对比 CT 扫描中分割出血区域。在分组胶囊网络中，我们约束了来自不同层中不同组输入胶囊的输出胶囊的预测胶囊。该方法可以减少中间预测胶囊的数量并加速胶囊网络。此外，我们修改了挤压函数，以在不影响其性能的情况下进一步加速前向过程。我们使用包含 210 个颅内出血 CT 扫描切片的采集数据集来评估我们提出的方法。在实验中，与现有方法相比，我们提出的方法在颅内出血区域分割方面取得了有竞争力的结果。
