ArticleProceedings. IEEE International Symposium on Biomedical Imaging2018
VOTING-BASED SEGMENTATION OF OVERLAPPING NUCLEI IN CLARITY IMAGES.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
New tissue-clearing techniques and improvements in optical microscopy have rapidly advanced capabilities to acquire volumetric imagery of neural tissue at resolutions of one micron or better. As sizes for data collections increase, accurate automatic segmentation of cell nuclei becomes increasingly important for quantitative analysis of imaged tissue. 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.