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Due to low tissue contrast, irregular object appearance, and unpredictablelocation variation, segmenting the objects from different medical imagingmodalities (e.g., CT, MR) is considered as an important yet challenging task.In this paper, we present a novel method for interactive medical imagesegmentation with the following merits. (1) Our design is fundamentallydifferent from previous pure patch-based and image-based segmentation methods.We observe that during delineation, the physician repeatedly check theinside-outside intensity changing to determine the boundary, which indicatesthat comparison in an inside-outside manner is extremely important. Thus, weinnovatively model our segmentation task as learning the representation of thebi-directional sequential patches, starting from (or ending in) the givencentral point of the object. This can be realized by our proposed ConvRNNnetwork embedded with a gated memory propagation unit. (2) Unlike previousinteractive methods (requiring bounding box or seed points), we only ask thephysician to merely click on the rough central point of the object beforesegmentation, which could simultaneously enhance the performance and reduce thesegmentation time. (3) We utilize our method in a multi-level framework forbetter performance. We systematically evaluate our method in three differentsegmentation tasks including CT kidney tumor, MR prostate, and PROMISE12challenge, showing promising results compared with state-of-the-art methods.The code is available here:\href{https://github.com/sunalbert/Sequential-patch-based-segmentation}{Sequential-patch-based-segmentation}.

Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning