论文标题
使用2D投影从3D MRI体积预测有效的大脑年龄预测
Efficient brain age prediction from 3D MRI volumes using 2D projections
论文作者
论文摘要
在高分辨率医疗量上使用3D CNN在计算上是非常要求的，尤其是对于诸如英国生物库之类的大型数据集，旨在扫描100,000名受试者。在这里，我们证明，在3D体积的几个2D投影（代表跨轴向，矢状和冠状切片的平均值和标准偏差）上使用2D CNN，在预测大脑体积的年龄时会导致合理的测试准确性。使用我们的方法，一个具有20,324名受试者的训练时期使用单个GPU需要20-50秒，与小3D CNN相比，该训练速度快两个数量级。这些结果对于无法使用3D CNN的昂贵GPU硬件的研究人员很重要。
Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20 - 50 seconds using a single GPU, which two orders of magnitude faster compared to a small 3D CNN. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.
