IEEE J Biomed Health Inform. 2019 Sep;23(5):1911-1919. doi: 10.1109/JBHI.2018.2874033. Epub 2018 Oct 4.
Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.
从 MRI 图像手动分割脑瘤以进行癌症诊断是一项困难、繁琐且耗时的任务。因此，脑肿瘤分割的准确性和稳健性对于诊断、治疗计划和治疗结果评估至关重要。大多数自动脑肿瘤分割方法使用手工设计的特征。同样，卷积神经网络等传统深度学习方法需要大量的标注数据进行学习，而这在医学领域往往难以获得。在这里，我们描述了一种新的双通道分组卷积神经网络模型，用于脑肿瘤分割，该模型同时利用局部特征和全局上下文特征。该模型在双通道 CNN 模型中强制等变，以减少不稳定性和过拟合参数共享。最后，我们将级联架构嵌入到双通道分组 CNN 中，其中基本 CNN 的输出被视为附加源，并在最后一层进行连接。在 BRATS2013 和 BRATS2015 数据集上对模型进行验证表明，在保持计算复杂度有吸引力的同时，将分组 CNN 嵌入到双通道架构中可以提高整体性能，优于目前已发布的最先进水平。
