Name
Title | ||
|---|---|---|
Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection |
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 Arabidopsis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363598 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | DocType | Volume |
Cell Segmentation,Convolutional Neural Networks,Developmental Biology,Arabidopsis,Meristem | Conference | abs/1710.06608 |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 1 |
PageRank | References | Authors |
0.36 | 6 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Stegmaier | 1 | 36 | 9.21 |
| Thiago Vallin Spina | 2 | 1 | 0.36 |
| Alexandre X. Falcão | 3 | 1877 | 132.30 |
| Andreas Bartschat | 4 | 14 | 3.48 |
| Ralf Mikut | 5 | 188 | 35.34 |
| Elliot M Meyerowitz | 6 | 55 | 11.09 |
| Alexandre Cunha | 7 | 2 | 1.04 |