Detection of Critical Structures in Scale Space

Pop out many small structures from a very large microscopic image

In medical research, many applications require counting and measuring small regions in a large image. Extracting these regions poses a dilemma in terms of segmentation granularity due to fine structures and segmentation complexity due to large image sizes. We propose a constrained spectral graph partitioning framework to address the former while also reducing the segmentation complexity associated with the latter. The final segmentation is obtained from a set of patch segmentations, each independently derived subject to stitching constraints between neighboring patches. Individual segmentation is based on local pairwise cues designed to pop out all cells simultaneously from their common background, while the constraints are derived from mutual agreement analysis on patch segmentations from a previous round of segmentation. Our results demonstrate that the constrained segmentation not only stitches solutions seamlessly along overlapping patch borders but also refines the segmentation in the patch interiors.

Segmentation subject to stitching constraints: finding many small structures in a large image

Extracting numerous cells in a large microscopic image is often required in medical research. The challenge is to reduce the segmentation complexity on a large image without losing the fine segmentation granularity of small structures. We propose a constrained spectral graph partitioning approach where the segmentation of the entire image is obtained from a set of patch segmentations, independently derived but subject to stitching constraints between neighboring patches. The constraints come from mutual agreement analysis on patch segmentations from a previous round. Our experimental results demonstrate that the constrained segmentation not only stitches solutions seamlessly along overlapping patch borders but also refines the segmentation in the patch interiors.