论文标题
具有变异自动编码器和MRI脑肿瘤分段的两阶段级联模型
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
论文作者
论文摘要
自动MRI脑肿瘤分割对于疾病诊断，监测和治疗计划至关重要。在本文中，我们提出了一个基于两阶段编码器的模型，用于脑肿瘤下区域分割。在两个阶段都使用变异自动编码器正则化来防止过度拟合问题。第二阶段网络采用注意门，并使用第一阶段输出形成的扩展数据集进行了训练。在BRAT 2020验证数据集上，所提出的方法的平均骰子得分为0.9041、0.8350和0.7958，以及Hausdorff距离（95％）的平均骰子分别为4.953、6.299和23.608，分别为整个Tumor，Tumor，Tumor核心和增强肿瘤。 Brats 2020测试数据集的相应结果为骰子得分0.8729、0.8357和0.8205，Hausdorff距离为11.4288、19.9690，为15.6711。该代码可在https://github.com/shu-hai/two-stage-vae-vae-citter-gate-brats2020上公开获得。
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. Variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953, 6.299, and 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://github.com/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.
