Name
Title | ||
|---|---|---|
Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms |
Abstract | ||
|---|---|---|
Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed tomography (CT) images, which is time-consuming and subjective. We used a semi-automatic deep learning (DL) method for segmentation of pNENs and verified its feasibility in radiomics analysis. This retrospective study included two datasets: Dataset 1, ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JBHI.2021.3070708 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Image segmentation,Radiomics,Pathology,Lesions,Training,Computed tomography,Testing | Journal | 25 |
Issue | ISSN | Citations |
9 | 2168-2194 | 1 |
PageRank | References | Authors |
0.40 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingsheng Huang | 1 | 3 | 1.10 |
| Xiaoyi Lin | 2 | 1 | 0.40 |
| Jingxian Shen | 3 | 1 | 0.40 |
| Xin Chen | 4 | 10 | 9.01 |
| Jia Chen | 5 | 1 | 0.40 |
| Zi-Ping Li | 6 | 1 | 0.40 |
| Mingyu Wang | 7 | 135 | 24.90 |
| Chenglang Yuan | 8 | 6 | 1.53 |
| Xian-Fen Diao | 9 | 1 | 0.40 |
| Yanji Luo | 10 | 3 | 0.76 |
| Shiting Feng | 11 | 3 | 0.76 |