Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
This addresses the challenge of reducing annotation effort for medical image segmentation, offering a practical solution for clinicians, though it is incremental in improving weakly supervised techniques.
The paper tackles the problem of 3D image segmentation with minimal annotations by proposing InExtremIS, which uses only 6 extreme clicks per object to train a network, achieving competitive performance close to full supervision and outperforming other weakly supervised methods on a Vestibular Schwannoma dataset.
We introduce $\textit{InExtremIS}$, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects of interest. Our fully-automatic method is trained end-to-end and does not require any test-time annotations. From the extreme points, 3D bounding boxes are extracted around objects of interest. Then, deep geodesics connecting extreme points are generated to increase the amount of "annotated" voxels within the bounding boxes. Finally, a weakly supervised regularised loss derived from a Conditional Random Field formulation is used to encourage prediction consistency over homogeneous regions. Extensive experiments are performed on a large open dataset for Vestibular Schwannoma segmentation. $\textit{InExtremIS}$ obtained competitive performance, approaching full supervision and outperforming significantly other weakly supervised techniques based on bounding boxes. Moreover, given a fixed annotation time budget, $\textit{InExtremIS}$ outperforms full supervision. Our code and data are available online.