title: Segmentation subject to stitching constraints: finding many small structures in a large image.
abstract: 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.
authors.affiliation: University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Elena
authors.initials: E
authors.lastname: Bernardis
authors.forename: Stella X
authors.initials: SX
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002478:Cells, Cultured / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique