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.

本文提出了一种深度学习交互分割方法，通过将用户交互与 CNN 结合，使用测地距离变换来提高自动 CNN 分割的精度和鲁棒性，并将用户交互作为硬约束集成到反向传播式 CRF 中，以实现在更高的精度下减少用户干预的目的。实验结果表明，与传统的交互式方法相比，该方法在自动 CNN 分割的基础上实现了大幅提升，并且在少数用户干预和更短的时间内具有可比甚至更高的准确性。