Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization
This addresses the annotation burden for experts in microscopy, offering a practical solution for point-supervised segmentation, though it is incremental as it builds on existing weak supervision methods.
The paper tackles the problem of reducing annotation effort for semantic segmentation of microscopy images and volumes by training networks with only a single point per instance, achieving competitive results against state-of-the-art methods on challenging datasets in digital pathology and scaling to 3D volumes.
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort. This work enables the training of semantic segmentation networks on images with only a single point for training per instance, an extreme case of weak supervision which drastically reduces the burden of annotation. Our approach has two key aspects: (1) we construct a graph-theoretic soft-segmentation using individual seeds to be used within a regularizer during training and (2) we use an objective function that enables learning from the constructed soft-labels. We achieve competitive results against the state-of-the-art in point-supervised semantic segmentation on challenging datasets in digital pathology. Finally, we scale our methodology to point-supervised segmentation in 3D fluorescence microscopy volumes, obviating the need for arduous manual volumetric delineation. Our code is freely available.