Name
Title | ||
|---|---|---|
Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images. |
Abstract | ||
|---|---|---|
Early diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce the effect of subjectivity and fatigue in manual annotation, computer-aided annotation is required. However, automated annotation of EEC lesions using images is a challenging task owing to the fine-grained variability in the appearance of EEC lesions. This study modifies the traditional EEC annotation framework and utilizes visual salient information to develop a two saliency levels-based lesion annotation (TSL-BLA) for EEC annotations on gastroscopic images. Unlike existing methods, the proposed framework has a strong ability of constraining false positive outputs. What is more, TSL-BLA is also placed an additional emphasis on the annotation of small EEC lesions. A total of 871 gastroscopic images from 231 patients were used to validate TSL-BLA. 365 of those images contain 434 EEC lesions and 506 images do not contain any lesions. 101 small lesion regions are extracted from the 434 lesions to further validate the performance of TSL-BLA. The experimental results show that the mean detection rate and Dice similarity coefficients of TSL-BLA were 97.24 and 75.15%, respectively. Compared with other state-of-the-art methods, TSL-BLA shows better performance. Moreover, it shows strong superiority when annotating small EEC lesions. It also produces fewer false positive outputs and has a fast running speed. Therefore, The proposed method has good application prospects in aiding clinical EEC diagnoses. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10916-018-1063-x | J. Medical Systems |
Keywords | Field | DocType |
Early esophageal cancer,Gastroscopic image,Lesion annotation,Superpixel segmentation,Visual saliency | Data mining,Annotation,Pattern recognition,Salience (neuroscience),Manual annotation,Artificial intelligence,Small Lesion,Medicine,Medical diagnosis,Superpixel segmentation,Visual saliency | Journal |
Volume | Issue | ISSN |
42 | 12 | 0148-5598 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ding-Yun Liu | 1 | 7 | 0.92 |
| Nini Rao | 2 | 14 | 2.46 |
| Xinming Mei | 3 | 0 | 0.34 |
| Hongxiu Jiang | 4 | 0 | 0.34 |
| Quanchi Li | 5 | 0 | 1.69 |
| Cheng-Si Luo | 6 | 7 | 0.92 |
| qian li | 7 | 26 | 23.56 |
| Chengshi Zeng | 8 | 0 | 0.34 |
| B Zeng | 9 | 1374 | 159.35 |
| Tao Gan | 10 | 7 | 0.92 |