Name
Title | ||
|---|---|---|
An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans |
Abstract | ||
|---|---|---|
Automatic segmentation of renal tumors and surrounding anatomy in computed tomography (CT) scans is a promising tool for assisting radiologists and surgeons in their efforts to study these scans and improve the prospect of treating kidney cancer. We describe our approach, which we used to compete in the 2021 Kidney and Kidney Tumor Segmentation (KiTS21) challenge. Our approach is based on the successful 3D U-Net architecture with our added innovations, including the use of transfer learning, an unsupervised regularized loss, custom postprocessing, and multi-annotator ground truth that mimics the evaluation protocol. Our submission has reached the 2nd place in the KiTS21 challenge. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-98385-7_14 | KIDNEY AND KIDNEY TUMOR SEGMENTATION, KITS 2021 |
Keywords | DocType | Volume |
Semantic segmentation, Medical imaging, 3D U-Net, Kidney tumor | Conference | 13168 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex Golts | 1 | 0 | 0.34 |
| Daniel Khapun | 2 | 0 | 0.34 |
| Daniel Shats | 3 | 0 | 0.34 |
| Yoel Shoshan | 4 | 0 | 1.01 |
| Flora Gilboa-Solomon | 5 | 0 | 0.34 |