Neurosurgery Center, Department of Cerebrovascular Surgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia.
Eur Radiol. 2022 Aug;32(8):5633-5641. doi: 10.1007/s00330-022-08608-7. Epub 2022 Feb 18.
We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data.
Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons.
Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system.
Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture.
• A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.
我们提出了一种新的方法，用于训练用于动脉瘤破裂预测的深度学习模型，该模型仅使用有限数量的标记数据。
使用分割的动脉瘤掩模作为输入，使用自监督方法对主干模型进行预训练，以从 947 个未经标记的血管造影图像中学习动脉瘤形态的深度嵌入。随后，使用 120 个具有已知破裂状态的标记病例对主干模型进行微调。将临床信息与深度嵌入相结合，以进一步提高预测性能。将所提出的模型与放射组学和常规形态模型在预测性能方面进行了比较。还基于该模型开发了辅助诊断系统，并由五名神经外科医生进行了测试。
我们的方法获得了接收器操作特征曲线（AUC）下的面积为 0.823，优于从头开始训练的深度学习模型（0.787）。通过与临床信息相结合，提出的模型的性能进一步提高到 AUC=0.853，使得结果明显优于基于放射组学的模型（AUC=0.805，p=0.007）或基于常规形态参数的模型（AUC=0.766，p=0.001）。我们的模型在其他方面也实现了最高的灵敏度、PPV、NPV 和准确性。使用辅助诊断系统，神经外科医生的预测性能从 AUC=0.877 提高到 0.945（p=0.037）。
我们提出的方法可以使用有限数量的数据开发用于破裂预测的竞争性深度学习模型。辅助诊断系统可帮助神经外科医生预测破裂。
提出了一种自监督学习方法来缓解深度学习对数据的需求问题，从而可以使用有限数量的数据训练深度神经网络。
使用所提出的方法提取了颅内动脉瘤形态的深度嵌入。基于深度嵌入的预测模型明显优于传统形态模型和放射组学模型。
使用基于深度嵌入的病例推理的辅助诊断系统开发，结果表明，该系统可显著提高神经外科医生预测破裂的能力。
