论文标题
使用不确定性估计的3D-CNN进行脑肿瘤分割
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation
论文作者
论文摘要
3D磁共振图像（MRI）中脑肿瘤的自动化是评估疾病诊断和治疗的关键。近年来，卷积神经网络（CNN）在任务中显示出改善的结果。但是，在3D-CNN中，高内存消耗仍然是一个问题。此外，大多数方法不包括不确定性信息，这在医学诊断中特别重要。这项工作提出了一个基于V-net \ cite {vnet}的3D编码器架构，该体系结构是通过修补技术训练的，以减少存储器消耗并降低不平衡数据的效果。我们还分别使用测试时间辍学和数据启发分别引入了Voxel的不确定性，无论是认识和核心。不确定性图可以为专家神经科医生提供额外的信息，可用于检测模型何时对所提供的细分不自信。
Automation of brain tumors in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is specially critical in medical diagnosis. This work proposes a 3D encoder-decoder architecture, based on V-Net \cite{vnet} which is trained with patching techniques to reduce memory consumption and decrease the effect of unbalanced data. We also introduce voxel-wise uncertainty, both epistemic and aleatoric using test-time dropout and data-augmentation respectively. Uncertainty maps can provide extra information to expert neurologists, useful for detecting when the model is not confident on the provided segmentation.
