Name
Title | ||
|---|---|---|
ACCURATE 3D KIDNEY SEGMENTATION USING UNSUPERVISED DOMAIN TRANSLATION AND ADVERSARIAL NETWORKS |
Abstract | ||
|---|---|---|
Computed tomography urography imaging is routinely performed to evaluate the kidneys. Kidney 3D segmentation and reconstruction from urographic images provides physicians with an intuitive visualization way to accurately diagnose and treat kidney diseases, particularly used in surgical planning and outcome analysis before and after kidney surgery. While 3D fully convolution networks have achieved a big success in medical image segmentation, they get trapped in clinical unseen data and cannot be adapted in deferent modalities with one training procedure. This study proposes an unsupervised domain adaptation or translation method with 2D networks to deeply learn urographic images for accurate kidney segmentation. We tested our proposed method on clinical urography data. The experimental results demonstrate our proposed method can resolve the domain shift problem of kidney segmentation and achieve the comparable or better results than supervised learning based segmentation methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISBI48211.2021.9434099 | 2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
Keywords | DocType | ISSN |
Kidney segmentation, CT urography, unsupervised domain adaptation, deep learning | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wankang Zeng | 1 | 0 | 1.35 |
| Wenkang Fan | 2 | 0 | 1.35 |
| Rong Chen | 3 | 0 | 0.34 |
| Zhuohui Zheng | 4 | 0 | 0.68 |
| Song Zheng | 5 | 0 | 0.34 |
| Jianhui Chen | 6 | 971 | 63.25 |
| Rong Liu | 7 | 0 | 0.34 |
| Zeng Qiang | 8 | 34 | 10.73 |
| Zengqin Liu | 9 | 0 | 0.34 |
| Yinran Chen | 10 | 0 | 1.35 |
| Xiongbiao Luo | 11 | 0 | 0.34 |