IEEE Trans Pattern Anal Mach Intell. 2019 Jul;41(7):1559-1572. doi: 10.1109/TPAMI.2018.2840695. Epub 2018 Jun 1.
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.
准确的医学图像分割对于诊断、手术规划和许多其他应用至关重要。卷积神经网络（CNN）已成为自动分割的最新方法。然而，完全自动的结果可能仍需要进一步改进，以使其足够准确和稳健，可用于临床。我们提出了一种基于深度学习的交互式分割方法，以提高自动 CNN 的结果，并减少细化过程中的用户交互，以提高准确性。我们使用一个 CNN 获得初始自动分割，然后在其上添加用户交互以指示错误分割。另一个 CNN 以用户交互和初始分割作为输入，给出细化结果。我们提出通过测地距离变换将用户交互与 CNN 结合起来，并提出一种分辨率保持网络，以提供更好的密集预测。此外，我们将用户交互作为硬约束集成到可反向传播的条件随机场中。我们在从胎儿 MRI 中分割 2D 胎盘和从 FLAIR 图像中分割 3D 脑肿瘤的背景下验证了所提出的框架。实验结果表明，与传统的交互式方法相比，我们的方法与自动 CNN 相比有很大的改进，并且与较少的用户干预和更少的时间相比，获得了可比甚至更高的准确性。
