论文标题
3D脑肿瘤分割的有效嵌入网络
Efficient embedding network for 3D brain tumor segmentation
论文作者
论文摘要
深度学习的3D医学图像处理极大地遭受了缺乏数据。因此，与与2D自然图像分析相关的工作相比，在该领域进行的研究受到限制。结果，已经开发和训练了强大而有效的2D卷积神经网络。在本文中，我们研究了一种转移二维分类网络的性能的方法，以进行三维语义分割的脑肿瘤。我们通过将有效网络模型纳入编码分支的一部分来提出一个不对称的U-NET网络。由于输入数据为3D，因此编码器的第一层专门用于缩小第三维，以适应EditiveNet网络的输入。验证和测试数据2020挑战的实验结果表明，所提出的方法实现了有希望的性能。
3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classiffication network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance.
