Offset Curves Loss for Imbalanced Problem in Medical Segmentation
| Autor: | Khoa Luu, Ngan Le, Kashu Yamazaki, T. H. N. Le, Toan Duc Bui, Marios Savides |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Offset (computer science) Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Regularization (mathematics) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Segmentation business.industry Deep learning Image and Video Processing (eess.IV) Pattern recognition Image segmentation Electrical Engineering and Systems Science - Image and Video Processing Term (time) Feature (computer vision) Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9411921 |
| Popis: | Medical image segmentation has played an important role in medical analysis and widely developed for many clinical applications. Deep learning-based approaches have achieved high performance in semantic segmentation but they are limited to pixel-wise setting and imbalanced classes data problem. In this paper, we tackle those limitations by developing a new deep learning-based model which takes into account both higher feature level i.e. region inside contour, intermediate feature level i.e. offset curves around the contour and lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss consists of three main fitting terms. The first fitting term focuses on pixel-wise level segmentation whereas the second fitting term acts as attention model which pays attention to the area around the boundaries (offset curves). The third terms plays a role as regularization term which takes the length of boundaries into account. We evaluate our proposed OsC loss on both 2D network and 3D network. Two common medical datasets, i.e. retina DRIVE and brain tumor BRATS 2018 datasets are used to benchmark our proposed loss performance. The experiments have shown that our proposed OsC loss function outperforms other mainstream loss functions such as Cross-Entropy, Dice, Focal on the most common segmentation networks Unet, FCN. ICPR 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|