论文标题
多模式MRI脑肿瘤分割的DR-UNET104
DR-Unet104 for Multimodal MRI brain tumor segmentation
论文作者
论文摘要
在本文中，我们提出了一个2D深残留的UNET，其中有104个卷积层（DR-UNET104），用于脑MRIS中的病变分割。我们对UNET体系结构进行了多种添加，包括将“瓶颈”残留块添加到UNET编码器中，并在每个卷积块堆栈后添加辍学。我们验证了以较小的速率（例如0.2）引入辍学的正规化的效果，并发现辍学率为0.2的辍学提高了整体性能，而没有辍学，或辍学的0.5。我们评估了所提出的结构，作为多模式脑肿瘤分割（BRAT）2020挑战的一部分，并将我们的方法与DeepLabV3+与Resnet-V2-152骨干链进行了比较。我们发现，用于验证数据，整个肿瘤，增强肿瘤和肿瘤核心的平均骰子得分系数为0.8862、0.6756和0.6721，平均骰子得分系数为0.8862、0.6756和0.6721，在0.8770、0.65242和0.68134方面的总体改进得到了DEEPLABV3+。我们的方法在整个肿瘤上产生了最终的平均DSC为0.8673、0.7514和0.7983，从而在挑战的测试数据上增强了肿瘤和肿瘤核心。尽管只有2D卷积，但我们还是产生了一种竞争性病变细分体系结构，其额外的好处是，它可以在低功率计算机上使用，而不是3D体系结构。这项工作的源代码和训练有素的模型可在https://github.com/jordan-colman/dr-unet104上公开获得。
In this paper we propose a 2D deep residual Unet with 104 convolutional layers (DR-Unet104) for lesion segmentation in brain MRIs. We make multiple additions to the Unet architecture, including adding the 'bottleneck' residual block to the Unet encoder and adding dropout after each convolution block stack. We verified the effect of introducing the regularisation of dropout with small rate (e.g. 0.2) on the architecture, and found a dropout of 0.2 improved the overall performance compared to no dropout, or a dropout of 0.5. We evaluated the proposed architecture as part of the Multimodal Brain Tumor Segmentation (BraTS) 2020 Challenge and compared our method to DeepLabV3+ with a ResNet-V2-152 backbone. We found that the DR-Unet104 achieved a mean dice score coefficient of 0.8862, 0.6756 and 0.6721 for validation data, whole tumor, enhancing tumor and tumor core respectively, an overall improvement on 0.8770, 0.65242 and 0.68134 achieved by DeepLabV3+. Our method produced a final mean DSC of 0.8673, 0.7514 and 0.7983 on whole tumor, enhancing tumor and tumor core on the challenge's testing data. We produced a competitive lesion segmentation architecture, despite only 2D convolutions, having the added benefit that it can be used on lower power computers than a 3D architecture. The source code and trained model for this work is openly available at https://github.com/jordan-colman/DR-Unet104.
