论文标题
MRI脑肿瘤分割和使用3D-UNET架构估计的不确定性估计
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures
论文作者
论文摘要
3D磁共振图像（MRI）中脑肿瘤分割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，大多数方法不包含不确定性信息，这在医学诊断中尤其重要。这项工作研究了经过基于补丁的技术训练的3D编码器架构，以减少记忆消耗并降低数据不平衡的效果。然后，使用不同的训练模型来创建一个合奏，以利用每个模型的属性，从而提高性能。我们还分别使用测试时间辍学（TTD）和数据提升（TTA）介绍了Voxel的不确定性信息。此外，提出了一种混合方法，有助于提高分割的准确性。这项工作中提出的模型和不确定性估计测量已用于任务1和3的Brats'20挑战中，涉及肿瘤分割和不确定性估计。
Automation of brain tumor segmentation 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 especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS'20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.
