Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
This work addresses the need for more accurate and efficient segmentation in biological imaging, offering a domain-specific improvement for researchers analyzing plant tissues.
The paper tackles the problem of automated 3D cell segmentation in microscopy images, particularly in deep tissue regions where current methods often fail, by introducing a supervoxel-based approach with CNN-based postprocessing that outperforms state-of-the-art methods and reduces manual correction effort, as validated on manually labeled 3D confocal images of Arabidopsis thaliana.
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.