Robust Seed Mask Generation for Interactive Image Segmentation
This addresses the inefficiency in medical image segmentation for clinicians by automating the initial seed generation, though it appears incremental as it builds on existing interactive methods.
The paper tackles the problem of time-consuming initial user interaction in interactive medical image segmentation by proposing an automatic seeding pipeline based on saliency recognition, achieving a median Dice score of 68.22% before the first user interaction with an error rate of 0.088%.
In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist of drawing pictorial hints as an initial estimate of the object to extract. Only after this time consuming first phase, the efficient selective refinement of current segmentation results begins. Erroneously labeled seeds, especially near the border of the object, are challenging to detect and replace for a human and may substantially impact the overall segmentation quality. We propose an automatic seeding pipeline as well as a configuration based on saliency recognition, in order to skip the time-consuming initial interaction phase during segmentation. A median Dice score of 68.22% is reached before the first user interaction on the test data set with an error rate in seeding of only 0.088%.