Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization

TL;DR

This paper introduces a novel weakly supervised method for microscopy image segmentation using only single-point annotations per instance, leveraging graph-based soft-segmentation and an innovative learning objective, applicable to 2D and 3D data.

Contribution

It presents a new approach for point-supervised segmentation that reduces annotation effort and scales to 3D microscopy volumes, outperforming existing methods.

Findings

Achieves competitive results on digital pathology datasets.

Enables segmentation in 3D microscopy volumes without manual delineation.

Uses graph-theoretic soft-segmentation with an effective learning objective.

Abstract

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.