论文标题
脊柱标志性的定位与热图回归和直接坐标回归结合
Spine Landmark Localization with combining of Heatmap Regression and Direct Coordinate Regression
论文作者
论文摘要
地标本地化在处理医学图像以及疾病鉴定中起着非常重要的作用。 However, In medical field, it's a challenging task because of the complexity of medical images and the high requirement of accuracy for disease identification and treatment.There are two dominant ways to regress landmark coordination, one using the full convolutional network to regress the heatmaps of landmarks , which is a complex way and heatmap post-process strategies are needed, and the other way is to regress the coordination using CNN + Full Connective Network directly, which is very simple and更快的培训，但是需要更大的数据集和更深的模型来实现更高的准确性。尽管随着数据的增强和更深的网络，它可以达到合理的精度，但是准确性仍然无法达到医疗领域的要求。此外，更深的网络还意味着更大的空间消耗。为了达到更高的精度，我们创造了一种新的地标回归方法，该方法将热图回归和直接坐标回归基础在概率方法和系统控制理论上。
Landmark Localization plays a very important role in processing medical images as well as in disease identification. However, In medical field, it's a challenging task because of the complexity of medical images and the high requirement of accuracy for disease identification and treatment.There are two dominant ways to regress landmark coordination, one using the full convolutional network to regress the heatmaps of landmarks , which is a complex way and heatmap post-process strategies are needed, and the other way is to regress the coordination using CNN + Full Connective Network directly, which is very simple and faster training , but larger dataset and deeper model are needed to achieve higher accuracy. Though with data augmentation and deeper network it can reach a reasonable accuracy, but the accuracy still not reach the requirement of medical field. In addition, a deeper networks also means larger space consumption. To achieve a higher accuracy, we contrived a new landmark regression method which combing heatmap regression and direct coordinate regression base on probability methods and system control theory.
