基于深度学习的自动化病变细分在儿科的焦点皮层发育不良II手术前MRI:一种可靠的方法
Siqi Zhang1,2, Yijiang Zhuang2, Yi Luo2
1Shantou University Medical College, Shantou University, 22 Xinling Road, Jinping District, Shantou, 515041, China.
Insights into imaging
|March 13, 2024
概括
深度学习模型现在可以使用MRI扫描自动检测和细分儿童的焦点皮层发育不良 (FCD) 病变. 这种AI方法提供了一种可靠的方法来识别FCD II,有助于的诊断.
科学领域:
- 医疗成像和人工智能的人工智能
- 儿童神经学和神经外科
- 计算神经科学是一种计算神经科学.
背景情况:
- 焦点皮层发育不良症 (FCD) 是儿科患者耐火性的主要原因.
- 深度学习 (DL) 算法在分析医疗成像以进行组织歧视方面表现有前途.
- 精确检测和细分FCD病变对于有效治疗至关重要.
研究的目的:
- 开发和验证3D全分辨率nnU-Net模型,用于自动检测和细分儿童的FCD II病变.
- 评估DL模型的性能与专家手动细分相比.
- 建立一个可靠的AI驱动工具,用于FCD病变的识别.
主要方法:
- 对65名患有病理确认FCD II的儿科患者的高分辨率脑MRI数据的回顾性分析.
- 经验丰富的神经放射学家手动对FCD病变进行细分,以确定基本真相.
- 使用3D全分辨率nnU-Net算法进行自动细分和病变检测.
- 使用五倍交叉验证进行培训和验证 (200次培训,15个测试样本).
- 使用Dice-Sørensen系数 (DSC) 和灵敏度进行性能评估,将AI输出与专家标签进行比较.
主要成果:
- 3D nnU-Net 模型在声素级 FCD 损伤检测方面表现良好,达到 0.73.3 的灵敏度.
- 最好的细分模型在测试数据集上获得了0.57的平均Dice-Sørensen系数 (DSC).
- 该算法成功处理了用于FCD II损伤细分的3D T1-MPRAGE数据.
结论:
- 3D全分辨率nnU-Net模型可靠地自动化了FCD病变的细分,为FCD检测提供了一种新的方法.
- 简化图像处理促进了DL模型的临床实施.
- 组织病理学确认的病变口罩增强了人工智能模型用于病变检测和决策的可信性和临床实用性.
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