26,198 research outputs found
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Automated segmentation approaches are crucial to quantitatively analyze
large-scale 3D microscopy images. Particularly in deep tissue regions,
automatic methods still fail to provide error-free segmentations. To improve
the segmentation quality throughout imaged samples, we present a new
supervoxel-based 3D segmentation approach that outperforms current methods and
reduces the manual correction effort. The algorithm consists of gentle
preprocessing and a conservative super-voxel generation method followed by
supervoxel agglomeration based on local signal properties and a postprocessing
step to fix under-segmentation errors using a Convolutional Neural Network. We
validate the functionality of the algorithm on manually labeled 3D confocal
images of the plant Arabidopis thaliana and compare the results to a
state-of-the-art meristem segmentation algorithm.Comment: 5 pages, 3 figures, 1 tabl
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