Name
Abstract | ||
|---|---|---|
Individualized and accurate segmentations of the prostate are essential for diagnosis as well as therapy planning in prostate cancer (PCa). Most of the previously proposed prostate segmentation approaches rely purely on axial MRI scans, which suffer from low out-of-plane resolution. We propose a method that makes use of sagittal and coronal MRI scans to improve the accuracy of segmentation. These scans are typically acquired as standard of care for PCa staging, but are generally ignored by the segmentation algorithms. Our method is based on a multi-stream 3D convolutional neural network for the automatic extraction of isotropic high resolution segmentations from MR images. We evaluated segmentation performance on an isotropic high resolution ground truth (n = 40 subjects). The results show that the use of multi-planar volumes for prostate segmentation leads to improved segmentation results not only for the whole prostate (92.1% Dice similarity coefficient), but also in apex and base regions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363549 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | Field | DocType |
MRI,prostate segmentation,deep convolutional neural networks,multi-stream architecture,multi-planar segmentation,3D planning,3D model generation | Computer vision,Coronal plane,Pattern recognition,Computer science,Convolutional neural network,Segmentation,Planar,Ground truth,Prostate cancer,Artificial intelligence,Prostate,Sagittal plane | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 1 |
PageRank | References | Authors |
0.35 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anneke Meyer | 1 | 5 | 2.13 |
| Alireza Mehrtash | 2 | 44 | 5.69 |
| Marko Rak | 3 | 31 | 8.37 |
| Daniel Schindele | 4 | 2 | 1.39 |
| Martin Schostak | 5 | 2 | 0.71 |
| Clare M Tempany | 6 | 629 | 45.11 |
| Tina Kapur | 7 | 390 | 45.30 |
| Purang Abolmaesumi | 8 | 951 | 111.52 |
| Andriy Fedorov | 9 | 171 | 16.54 |
| Christian Hansen | 10 | 191 | 31.27 |