Boundary-aware Segmentation Network Using Multi-Task Enhancement for Ultrasound Image

Complicated medical image analysis often requires a combination of disease classification, lesion detection and lesion segmentation. However, models designed for different tasks produce inconsistent or non-corresponding predictions and ignore the implicit connections between tasks. We propose a novel framework, which makes full use of the fact that segmentation and detection are mutually beneficial, boosts these three tasks in a unified framework. The proposed Information Enhancement Module uses classification information as a beneficial supplement to locate lesion quickly for segmentation. To further achieve fine segmentation with clear boundaries, we propose a Boundary-aware Loss, which dynamically adjusts supervised signal, so that our model pays more attention to boundary in later stages of training. Through experiments conducted on Thyroid Ultrasound dataset, we have demonstrated the good performance of the proposed method in joint segmentation and detection.