As a basic task in computer vision, semantic segmentation can provide
fundamental information for object detection and instance segmentation to help
the artificial intelligence better understand real world. Since the proposal of
fully convolutional neural network (FCNN), it has been widely used in semantic
segmentation because of its high accuracy of pixel-wise classification as well
as high precision of localization. In this paper, we apply several famous FCNN
to brain tumor segmentation, making comparisons and adjusting network
architectures to achieve better performance measured by metrics such as
precision, recall, mean of intersection of union (mIoU) and dice score
coefficient (DSC). The adjustments to the classic FCNN include adding more
connections between convolutional layers, enlarging decoders after up sample
layers and changing the way shallower layers' information is reused. Besides
the structure modification, we also propose a new classifier with a
hierarchical dice loss. Inspired by the containing relationship between
classes, the loss function converts multiple classification to multiple binary
classification in order to counteract the negative effect caused by imbalance
data set. Massive experiments have been done on the training set and testing
set in order to assess our refined fully convolutional neural networks and new
types of loss function. Competitive figures prove they are more effective than
their predecessors.

本研究采用多种流行的 FCNN 模型，在其基础上进行网络结构调整并使用分层 Dice 损失函数来提高基于 MRI 图像的脑肿瘤分割效果。经过大量实验对比，验证了新型网络和损失函数相比于传统方法在精度、召回率、mIoU 和 DSC 等性能指标上的有效性。