1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
2. Tianjin Medical University, Tianjin, China.
3. Affiliated Zhongshan Hospital of Dalian University, Department of Radiology, Dalian, Liaoning, China.
4. Department of New Networks, Pengcheng Laboratory, Shenzhen, China.
5. Department of Rehabilitation Radiology, Beijing Rehabilitation Hospital of Capital Medical University, Shijinshan District, China
6. The First People's Hospital of FoShan, Chancheng District, Foshan, China
Focal Boundary Dice, a new segmentation evaluation measure, was hereby presented, with the focus on boundary quality and class imbalance. Extensive analysis was carried out across different error types with varied object sizes of imaged tumors from Magnetic Resonance Imaging (MRI) scans, and the results show that Focal Boundary Dice is significantly more adaptive than the standard Focal and Dice measures to boundary errors for imaged tumors from MRI scans and does not over-penalize errors on the division of the boundary, including smaller imaged objects. Based on Boundary Dice, the standard evaluation protocols for tumor segmentation tasks were updated by proposing the Focal Boundary Dice. The contradiction between the target and the background area, and the conflict between the importance and the attention of boundary features were mainly solved. Meanwhile, a boundary attention module was introduced to further extract the tumor edge features. The new quality measure presents several desirable characteristics, including higher accuracy in the selection of hard samples, prediction/ground-truth pairs, and balanced responsiveness with across scales, which jointly make it more suitable for segmentation evaluation than other classification-focused measures such as combined Intersection-over-Union and Boundary binary cross-entropy loss, Boundary binary cross-entropy loss and Shape-aware Loss. The experiments show that the new evaluation metrics allow boundary quality improvements and image segmentation accuracy that are generally overlooked by current Dice-based evaluation metrics and deep learning models. It is expected that the adoption of the new boundary-adaptive evaluation metrics will facilitate the rapid progress in segmentation methods, and further contribute to the improvement of classification accuracy.
Keywords: medical image segmentation, deep learning, boundary binary cross-entropy, Magnetic Resonance Imaging, dice loss, Intersection-over-Union loss, Tversky loss.