Voting-based segmentation of overlapping nuclei in clarity images.

We present a cell nucleus segmentation method that is formulated as a parameter estimation problem with the goal of determining the count, shapes, and locations of nuclei that most accurately describe an image. We applied our new voting-based approach to fluorescence confocal microscopy images of neural tissue stained with DAPI, which highlights nuclei. Compared to manual counting of cells in three DAPI images, our method outperformed three existing approaches. On a manually labeled high-resolution DAPI image, our method also outperformed those methods and achieved a cell count accuracy of 98.99% and mean Dice coefficient of 0.6498.
PMID: 32038768 [PubMed]

Source: Proceedings - International Symposium on Biomedical Imaging - February 12, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research