Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation
复制标题
基于深度学习的视网膜血管分割的联合分段级和像素级损失
DOI：
10.1109/tbme.2018.2828137
复制
发表时间：
2018-09-01
影响因子：
4.6
通讯作者：
Cheng, Kwang-Ting
中科院分区：
文献类型：
--
作者：
Yan, Zengqiang;Yang, Xin;Cheng, Kwang-Ting
目的：用于视网膜血管分割的基于深度学习的方法通常基于像素损失进行训练，其在预测的概率图和对应的手动注释的分割之间的像素到像素匹配中以同等重要性对待所有血管像素。然而，由于眼底图像中粗血管和细血管之间的像素比例高度不平衡，像素级丢失将限制深度学习模型学习用于精确分割细血管的特征，这是眼部相关疾病临床诊断的重要任务。研究方法：在本文中，我们提出了一个新的段级损失，更强调在训练过程中的薄血管的厚度一致性。通过联合采用节段级和像素级损失，损失计算中厚血管和薄血管之间的重要性将更加平衡。因此，可以学习更有效的特征用于血管分割，而不会增加整体模型的复杂性。结果如下：在公共数据集上的实验结果表明，联合损失训练的模型在单独训练和交叉训练评估中的性能优于当前最先进的方法。结论：与逐像素损失相比，利用所提出的联合损失框架能够学习用于血管分割的更多可区分的特征。此外，段级丢失可以为深层和浅层网络架构带来一致的性能改善。意义：这项使用联合损失的研究结果可以应用于其他深度学习模型，以提高性能，而不会显着改变网络架构。
Objective: Deep learning based methods for retinal vessel segmentation are usually trained based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to-pixel matching between a predicted probability map and the corresponding manually annotated segmentation. However, due to the highly imbalanced pixel ratio between thick and thin vessels in fundus images, a pixel-wise loss would limit deep learning models to learn features for accurate segmentation of thin vessels, which is an important task for clinical diagnosis of eye-related diseases. Methods: In this paper, we propose a new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process. By jointly adopting both the segment-level and the pixel-wise losses, the importance between thick and thin vessels in the loss calculation would be more balanced. As a result, more effective features can be learned for vessel segmentation without increasing the overall model complexity. Results: Experimental results on public data sets demonstrate that the model trained by the joint losses outperforms the current state-of-the-art methods in both separate-training and cross-training evaluations. Conclusion: Compared to the pixel-wise loss, utilizing the proposed joint-loss framework is able to learn more distinguishable features for vessel segmentation. In addition, the segment-level loss can bring consistent performance improvement for both deep and shallow network architectures. Significance: The findings from this study of using joint losses can be applied to other deep learning models for performance improvement without significantly changing the network architectures.