Glioma is one of the most common and aggressive types of primary brain
tumors. The accurate segmentation of subcortical brain structures is crucial to
the study of gliomas in that it helps the monitoring of the progression of
gliomas and aids the evaluation of treatment outcomes. However, the large
amount of required human labor makes it difficult to obtain the manually
segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise
quantitative measurements in the clinical practice. In this work, we try to
address this problem by developing a 3D Convolutional Neural Network~(3D CNN)
based model to automatically segment gliomas. The major difficulty of our
segmentation model comes with the fact that the location, structure, and shape
of gliomas vary significantly among different patients. In order to accurately
classify each voxel, our model captures multi-scale contextual information by
extracting features from two scales of receptive fields. To fully exploit the
tumor structure, we propose a novel architecture that hierarchically segments
different lesion regions of the necrotic and non-enhancing tumor~(NCR/NET),
peritumoral edema~(ED) and GD-enhancing tumor~(ET). Additionally, we utilize
densely connected convolutional blocks to further boost the performance. We
train our model with a patch-wise training schema to mitigate the class
imbalance problem. The proposed method is validated on the BraTS 2017 dataset
and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor,
tumor core and enhancing tumor, respectively. These results are comparable to
the reported state-of-the-art results, and our method is better than existing
3D-based methods in terms of compactness, time and space efficiency.

本研究提出了基于 3D CNN 的新型分割网络对 Glioma 进行自动分割，该方法利用 MRI 数据帮助病灶分割，分类准确率较高，是一种有效和高效的病灶分割方法，可以帮助研究和评估 Glioma 的治疗效果。