Due to low tissue contrast, irregular shape, and large location variance, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, a novel method is presented for interactive medical image segmentation with the following merits. (1) Its design is fundamentally different from previous pure patch-based and image-based segmentation methods. It is observed that during delineation, the physician repeatedly check the intensity from area inside-object to outside-object to determine the boundary, which indicates that comparison in an inside-out manner is extremely important. Thus, the method innovatively models the segmentation task as learning the representation of bi-directional sequential patches, starting from (or ending in) the given central point of the object. This can be realized by the proposed ConvRNN network embedded with a gated memory propagation unit. (2) Unlike previous interactive methods (requiring bounding box or seed points), the proposed method only asks the physician to merely click on the rough central point of the object before segmentation, which could simultaneously enhance the performance and reduce the segmentation time. (3) The method is utilized in a multi-level framework for better performance. It has been systematically evaluated in three different segmentation tasks, including CT kidney tumor, MR prostate, and PROMISE12 challenge, showing promising results compared with state-of-the-art methods.

由于组织对比度低、形状不规则和位置差异大，从不同的医学成像模式（例如CT、MR）分割对象被认为是一项重要但具有挑战性的任务。在本文中，提出了一种交互式医学图像分割的新方法，具有以下优点。(1) 它的设计与之前的纯基于补丁和基于图像的分割方法有着根本的不同。观察到，在勾画过程中，医生反复检查区域内物体到外物体的强度来确定边界，这表明由内而外的方式比较是非常重要的. 因此，该方法创新地将分割任务建模为学习双向顺序补丁的表示，从（或结束于）对象的给定中心点开始。这可以通过嵌入门控内存传播单元的建议 ConvRNN 网络来实现。(2) 不同于以往的交互方法（需要边界框或种子点），该方法只要求医生在分割前只需点击对象的粗略中心点，可以同时提高性能并减少分割时间。(3) 该方法用于多级框架以获得更好的性能。它已经在三个不同的分割任务中进行了系统评估，包括 CT 肾肿瘤、MR 前列腺和 PROMISE12 挑战，