Enhancing Medical Image Segmentation with a Lightweight Boundary-Aware Multitask Detection Head

Medical image segmentation is a pivotal technology for improving diagnostic accuracy and treatment outcomes and is critical for accurate lesion localization. Deep learning methods have become a significant tool for processing complex biomedical image data and have contributed significantly to the development of imaging segmentation techniques. However, ambiguous object boundaries and finite annotated samples tend to restrict the best performance of the models. To overcome these challenges, in this paper, we present a novel plug-and-play lightweight boundary-aware multitask detection head to improve the segmentation performance of deep learning models. Specifically, this module integrates Boundary Detection and Signed Distance Map as auxiliary tasks to efficiently utilize the pixel-level annotation information, which significantly improves the ability of the deep learning models to recognize and precisely delineate target boundaries, thereby enhancing segmentation performance. The experiments on two public segmentation datasets demonstrated that our proposed detection head outperforms traditional segmentation heads in the evaluation metrics with a slight increase in model parameters. These results not only verify the efficacy of our proposed model in improving the accuracy of boundary detection but also provide valuable insights for achieving more accurate medical image segmentation using deep learning models.