Name
Abstract | ||
|---|---|---|
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with differ-ent quantitative metrics, which result in discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, and to determine the performance of the best breast tumor segmentation algorithm available today and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, we will publish a B-mode BUS image segmentation benchmark (BUSIS) with 562 images and compare the performance of five state-of-the-art BUS segmentation methods quantitatively. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Breast ultrasound,CAD,Breast tumor,Pattern recognition,Computer science,Segmentation,Clinical Practice,Image segmentation,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1801.03182 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Xian | 1 | 21 | 5.84 |
| Yingtao Zhang | 2 | 95 | 12.27 |
| H. D. Cheng | 3 | 1900 | 138.13 |
| Fei Xu | 4 | 11 | 12.78 |
| Kuan Huang | 5 | 1 | 3.41 |
| boyu zhang | 6 | 71 | 17.54 |
| Jianrui Ding | 7 | 27 | 6.26 |
| Chunping Ning | 8 | 0 | 0.68 |
| Ying Wang | 9 | 0 | 1.35 |