  Due to low tissue contrast, irregular object appearance, and unpredictable
location variation, segmenting the objects from different medical imaging
modalities (e.g., CT, MR) is considered as an important yet challenging task.
In this paper, we present a novel method for interactive medical image
segmentation with the following merits. (1) Our design is fundamentally
different from previous pure patch-based and image-based segmentation methods.
We observe that during delineation, the physician repeatedly check the
inside-outside intensity changing to determine the boundary, which indicates
that comparison in an inside-outside manner is extremely important. Thus, we
innovatively model our segmentation task as learning the representation of the
bi-directional sequential patches, starting from (or ending in) the given
central point of the object. This can be realized by our proposed ConvRNN
network embedded with a gated memory propagation unit. (2) Unlike previous
interactive methods (requiring bounding box or seed points), we only ask 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) We utilize our method in a multi-level framework for
better performance. We systematically evaluate our method in three different
segmentation tasks including CT kidney tumor, MR prostate, and PROMISE12
challenge, 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}.
