Multi-Mode Interactive Image Segmentation

Large-scale pixel-level annotations are scarce for current data-hungry medical image analysis models. For the fast acquisition of annotations, an economical and efficient interactive medical image segmentation method is urgently needed. However, current techniques usually fail in many cases, as their interaction styles cannot work on various inherent ambiguities of medical images, such as irregular shapes and fuzzy boundaries. To address this problem, we propose a multi-mode interactive segmentation framework for medical images, where diverse interaction modes can be chosen and allowed to cooperate with each other. In our framework, users can encircle the target regions with various initial interaction modes according to the structural complexity. Then, based on the initial segmentation, users can jointly utilize the region and boundary interactions to refine the mislabeled regions caused by different ambiguities. We evaluate our framework on extensive medical images, including X-ray, CT, MRI, ultrasound, endoscopy, and photo. Sufficient experimental results and user study show that our framework is a reliable choice for image annotation in various real scenes.