PDF(Pubmed)
Abstract：
A major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition techniques and large variations across the patient spectrum. The result is poor translation of the trained models to the clinic, which limits their implementation in medical practice. Patient-specific trained networks could provide a potential solution. Although patient-specific approaches are usually infeasible because of the expenses associated with on-the-fly labeling, the use of generative adversarial networks enables this approach. This study proposes a patient-specific approach based on generative adversarial networks. In the presented training pipeline, the user trains a patient-specific segmentation network with extremely limited data which is supplemented with artificial samples generated by generative adversarial models. This approach is demonstrated in endoscopic video data captured during fetoscopic laser coagulation, a procedure used for treating twin-to-twin transfusion syndrome by ablating the placental blood vessels. Compared to a standard deep learning segmentation approach, the pipeline was able to achieve an intersection over union score of 0.60 using only 20 annotated images compared to 100 images using a standard approach. Furthermore, training with 20 annotated images without the use of the pipeline achieves an intersection over union score of 0.30, which, therefore, corresponds to a 100% increase in performance when incorporating the pipeline. A pipeline using GANs was used to generate artificial data which supplements the real data, this allows patient-specific training of a segmentation network. We show that artificial images generated using GANs significantly improve performance in vessel segmentation and that training patient-specific models can be a viable solution to bring automated vessel segmentation to the clinic.
摘要：
将机器学习应用于医学领域的主要障碍是训练图像的数据分布与诊所中遇到的数据之间的差异。这种现象可以通过不一致的采集技术和跨患者频谱的大变化来解释。结果是训练过的模型在临床上的翻译很差，这限制了它们在医疗实践中的实施。特定于患者的经过训练的网络可以提供潜在的解决方案。尽管由于与即时标签相关的费用，针对患者的方法通常不可行，使用生成对抗网络可以实现这种方法。本研究提出了一种基于生成对抗网络的针对患者的方法。在提出的培训管道中，用户使用极其有限的数据训练患者特定的分割网络，该网络补充了由生成对抗模型生成的人工样本。在胎儿镜激光凝固过程中捕获的内窥镜视频数据中证明了这种方法，一种通过切除胎盘血管治疗双胎对双胎输血综合征的方法。与标准的深度学习分割方法相比，与使用标准方法的100张图像相比，仅使用20张注释图像，管道就能够实现0.60的联合得分相交。此外，在不使用管道的情况下，用20个带注释的图像进行训练，获得了0.30的联合分数的交点，因此,对应于合并管道时性能的100%提高。使用GAN的管道用于生成补充真实数据的人工数据，这允许对分割网络进行患者特定的训练。我们表明，使用GAN生成的人工图像显着提高了血管分割的性能，并且训练患者特定的模型可以成为将自动血管分割带入临床的可行解决方案。
