| Paper ID | BIO-6.5 |
| Paper Title |
A HYBRID FEATURE ENHANCEMENT METHOD FOR GLAND SEGMENTATION IN HISTOPATHOLOGY IMAGES |
| Authors |
Xiangjiang Wu, Xiangtan University, China; Xuanya Li, Baidu Inc., China; Kai Hu, Xiangtan University, China; Zhineng Chen, Fudan University, China; Xieping Gao, Xiangtan University, China |
| Session | BIO-6: Medical Image Segmentation |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-MIA] Medical image analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Accurate and automatic gland segmentation can help pathologists diagnose the malignancy of colorectal cancers. However, it remains a challenging task because of the large morphological differences between the glands and the presence of sticky glands. In this paper, a hybrid feature enhancement network (HFE-Net) for glandular segmentation is proposed, which includes a multi-scale local feature extraction block (MSLFEB) and a global feature enhancement block (GFEB). Specifically, the MSLFEB is used to extract multi-scale features through different sizes of the receptive field to reduce the loss of the local information and effectively alleviate glandular adhesion. The GFEB is used to transfer the underlying features to the decoder by considering the global semantic information. Furthermore, we design a focal and variance (FV) loss function to alleviate the class imbalance and constraint the pixels within the same instance. Finally, we evaluate the proposed method on the 2015 MICCAI GlaS challenge dataset and the CRAG colorectal adenocarcinoma dataset. The results show that our HFE-Net can achieve competitive results with fewer computing resources when compared with the state-of-the-art gland segmentation methods. |
