Thesedays, Convolutional Neural Networks are widely used in semantic
segmentation. However, since CNN-based segmentation networks produce
low-resolution outputs with rich semantic information, it is inevitable that
spatial details (e.g., small bjects and fine boundary information) of
segmentation results will be lost. To address this problem, motivated by a
variational approach to image segmentation (i.e., level set theory), we propose
a novel loss function called the level set loss which is designed to refine
spatial details of segmentation results. To deal with multiple classes in an
image, we first decompose the ground truth into binary images. Note that each
binary image consists of background and regions belonging to a class. Then we
convert level set functions into class probability maps and calculate the
energy for each class. The network is trained to minimize the weighted sum of
the level set loss and the cross-entropy loss. The proposed level set loss
improves the spatial details of segmentation results in a time and memory
efficient way. Furthermore, our experimental results show that the proposed
loss function achieves better performance than previous approaches.

研究使用 CNN 进行语义分割时由于输出分辨率较低而损失空间细节的问题，采用基于 level set 理论的新型 loss function：level set loss，将 level set 函数转化为类别概率图，通过加权求和与 cross-entropy loss 进行联合优化，提高了分割结果的空间细节。对比先前的方法取得更好的性能。