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arXiv · 1710.06608

Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection

Johannes Stegmaier·Thiago V. Spina·Alexandre X. Falcão·Andreas Bartschat·Ralf Mikut·Elliot Meyerowitz·Alexandre Cunha
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Abstract

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.

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Johannes Stegmaier, Thiago V. Spina, Alexandre X. Falcão, Andreas Bartschat, Ralf Mikut, Elliot Meyerowitz, Alexandre Cunha. 2017-10-18. Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection. https://arxiv.org/abs/1710.06608

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