Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automatic segmentation, they are often limited by the lack of clinically acceptable accuracy and robustness in complex cases. Therefore, interactive segmentation is a practical alternative to these methods. However, traditional interactive segmentation methods require a large amount of user interactions, and recently proposed CNN-based interactive segmentation methods are limited by poor performance on previously unseen objects. To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects. Specifically, we first encode user-provided interior margin points via our proposed exponentialized geodesic distance that enables a CNN to achieve a good initial segmentation result of both previously seen and unseen objects, then we use a novel information fusion method that combines the initial segmentation with only few additional user clicks to efficiently obtain a refined segmentation. We validated our proposed framework through extensive experiments on 2D and 3D medical image segmentation tasks with a wide range of previous unseen objects that were not present in the training set. Experimental results showed that our proposed framework 1) achieves accurate results with fewer user interactions and less time compared with state-of-the-art interactive frameworks and 2) generalizes well to previously unseen objects.

翻译：诊断和治疗规划等许多临床应用,诊断和治疗规划等诊断和治疗规划等诊断性应用中,器官的器官或医疗图象的损伤的分解具有重要作用。虽然进化神经网络(CNN)已经实现了自动分解的最先进的性能,但往往由于在复杂案例中缺乏临床可接受的准确性和稳健性而受到限制。因此,交互分解是替代这些方法的一个实用选择。然而,传统的交互分解方法需要大量的用户互动,而最近提出的以CNN为基础的交互分解方法则由于先前看不见的物体的性能不佳而受到限制。为了解决这些问题,我们建议采用一种新的深层次的基于学习的交互分解方法,不仅由于仅仅需要点击用户投入,而且还能很好地概括一些先前看不见的物体,因而效率很高。具体地说,我们首先通过我们提议的指数化的地理分解距离来编码用户提供的内部差点,使CNN能够取得一个良好的初始分解结果,然后我们使用一种新的信息混合方法,将最初的分解与仅有的少量用户点击来有效取得更精确的分解。我们提出的框架,而现在又通过广泛的数字分解的分解式框架比起来,我们提出的框架在以前的深层次上比了一种广泛的分解的结果。