First Affiliated Hospital, Gannan Medical University, Ganzhou, China.
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Comput Intell Neurosci. 2022 Apr 14;2022:5333589. doi: 10.1155/2022/5333589. eCollection 2022.
Intracranial aneurysms are local dilations of the cerebral blood vessels; people with intracranial aneurysms have a high risk to cause bleeding in the brain, which is related to high mortality and morbidity rates. Accurate detection and segmentation of intracranial aneurysms from Magnetic Resonance Angiography (MRA) images are essential in the clinical routine. Manual annotations used to assess the intracranial aneurysms on MRA images are substantial interobserver variability for both aneurysm detection and assessment of aneurysm size and growth. Many prior automated segmentation works have focused their efforts on tackling the problem, but there is still room for performance improvement due to the significant variability of lesions in the location, size, structure, and morphological appearance. To address these challenges, we propose a novel One-Two-One Fully Convolutional Networks (OTO-Net) for intracranial aneurysms automated segmentation in MRA images. The OTO-Net uses full convolution to achieve intracranial aneurysms automated segmentation through the combination of downsampling, upsampling, and skip connection. In addition, loss ensemble is used as the objective function to steadily improve the backpropagation efficiency of the network structure during the training process. We evaluated the proposed OTO-Net on one public benchmark dataset and one private dataset. Our proposed model can achieve the automated segmentation accuracy with 98.37% and 97.86%, average surface distances with 1.081 and 0.753, dice similarity coefficients with 0.9721 and 0.9813, and Hausdorff distance with 0.578 and 0.642 on these two datasets, respectively.
颅内动脉瘤是脑血管的局部扩张；颅内动脉瘤患者有很高的脑出血风险，这与高死亡率和高发病率有关。从磁共振血管造影 (MRA) 图像中准确检测和分割颅内动脉瘤对于临床常规非常重要。用于评估 MRA 图像上颅内动脉瘤的手动注释存在很大的观察者间变异性，无论是动脉瘤的检测还是动脉瘤大小和生长的评估。许多先前的自动分割工作都集中精力解决这个问题，但由于病变在位置、大小、结构和形态外观上的显著可变性，仍有提高性能的空间。为了解决这些挑战，我们提出了一种新的用于 MRA 图像中颅内动脉瘤自动分割的 One-Two-One 全卷积网络 (OTO-Net)。OTO-Net 使用全卷积通过下采样、上采样和跳过连接的组合来实现颅内动脉瘤的自动分割。此外，损失集成被用作目标函数，以在训练过程中稳定地提高网络结构的反向传播效率。我们在一个公共基准数据集和一个私人数据集上评估了所提出的 OTO-Net。我们提出的模型可以在这两个数据集上分别实现 98.37%和 97.86%的自动分割精度、1.081 和 0.753 的平均表面距离、0.9721 和 0.9813 的骰子相似系数以及 0.578 和 0.642 的 Hausdorff 距离。
