论文标题
脑肿瘤MR图像的合成用于学习数据增强
Synthesis of Brain Tumor MR Images for Learning Data Augmentation
论文作者
论文摘要
使用深神网络的医学图像分析已被积极研究。深度神经网络通过学习数据训练。为了准确培训深层神经网络，学习数据应足够，质量良好，并且应该具有广义性。但是，在医学图像中，由于患者招募的困难，专家对病变的注释负担以及患者隐私的侵害，因此很难获得足够的患者数据。相比之下，可以轻松获取健康志愿者的医学图像。使用健康的大脑图像，提出的方法合成了脑肿瘤的多对比磁共振图像。由于肿瘤具有复杂的特征，因此提出的方法将它们简化为易于控制的同心圆。然后，它通过深层神经网络将同心圆转化为肿瘤的各种逼真的形状。由于很容易获得许多健康的大脑图像，因此我们的方法可以与各种同心圆合成大量的脑肿瘤图像。我们进行了定性和定量分析，以评估提出方法的增强数据的实用性。直观且有趣的实验结果可在线访问https://github.com/ksh0660/braintumor
Medical image analysis using deep neural networks has been actively studied. Deep neural networks are trained by learning data. For accurate training of deep neural networks, the learning data should be sufficient, of good quality, and should have a generalized property. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients' privacy. In comparison, the medical images of healthy volunteers can be easily acquired. Using healthy brain images, the proposed method synthesizes multi-contrast magnetic resonance images of brain tumors. Because tumors have complex features, the proposed method simplifies them into concentric circles that are easily controllable. Then it converts the concentric circles into various realistic shapes of tumors through deep neural networks. Because numerous healthy brain images are easily available, our method can synthesize a huge number of the brain tumor images with various concentric circles. We performed qualitative and quantitative analysis to assess the usefulness of augmented data from the proposed method. Intuitive and interesting experimental results are available online at https://github.com/KSH0660/BrainTumor
