Background:
Glioma is one of the most common and aggressive primary brain tumors
that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and
treatment of cancer disease. However, it is a relatively challenging task to precisely segment tumors
considering characteristics of brain tumors and the device noise. Recently, with the breakthrough
development of deep learning, brain tumor segmentation methods based on fully convolutional
neural network (FCN) have illuminated brilliant performance and attracted more and more
attention.
Methods:
In this work, we propose a novel FCN based network called SDResU-Net for brain tumor
segmentation, which simultaneously embeds dilated convolution and separable convolution
into residual U-Net architecture. SDResU-Net introduces dilated block into a residual U-Net architecture,
which largely expends the receptive field and gains better local and global feature descriptions
capacity. Meanwhile, to fully utilize the channel and region information of MRI brain
images, we separate the internal and inter-slice structures of the improved residual U-Net by employing
separable convolution operator. The proposed SDResU-Net captures more pixel-level details
and spatial information, which provides a considerable alternative for the automatic and accurate
segmentation of brain tumors.
Results and Conclusion:
The proposed SDResU-Net is extensively evaluated on two public MRI
brain image datasets, i.e., BraTS 2017 and BraTS 2018. Compared with its counterparts and stateof-
the-arts, SDResU-Net gains superior performance on both datasets, showing its effectiveness.
In addition, cross-validation results on two datasets illuminate its satisfying generalization ability.
Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model
