Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

TL;DR

This paper presents InExtremIS, a weakly supervised 3D image segmentation method that uses only six extreme boundary clicks, leveraging geodesics and CRF-based regularization to achieve near full supervision performance.

Contribution

The paper introduces a novel end-to-end weakly supervised segmentation approach using extreme points, geodesics, and CRF regularization, requiring minimal annotations.

Findings

Achieves competitive segmentation performance close to fully supervised methods.

Significantly outperforms other weakly supervised techniques based on bounding boxes.

Outperforms full supervision given the same annotation time budget.

Abstract

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.