Name
Abstract | ||
|---|---|---|
Automatic analysis of histopathological whole slide images (WSIs) is a challenging task. In this paper, we designed two deep learning structures based on a fully convolutional network (FCN) and a convolutional neural network (CNN), to achieve the segmentation of carcinoma regions from WSIs. FCN is developed for segmentation problems and CNN focuses on classification. We designed experiments to compare the performances of the two methods. The results demonstrated that CNN performs as well as FCN when applied to WSIs in high resolution. Furthermore, to leverage the advantages of CNN and FCN, we integrate the two methods to obtain a complete framework for lung cancer segmentation. The proposed methods were evaluated on the ACDC-LungHP dataset. The final dice coefficient for cancerous region segmentation is 0.770. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-34110-7_47 | IMAGE AND GRAPHICS, ICIG 2019, PT II |
Keywords | DocType | Volume |
Image segmentation, Computational pathology, CNN, FCN, Lung cancer | Conference | 11902 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shujiao Sun | 1 | 1 | 1.04 |
| Bonan Jiang | 2 | 2 | 0.75 |
| Yushan Zheng | 3 | 0 | 0.34 |
| Fengying Xie | 4 | 182 | 15.33 |