Annotation-efficient deep learning for automatic medical image segmentation

Wang, Shanshan; Li, Cheng; Wang, Rongpin; Liu, Zaiyi; Wang, Meiyun; Tan, Hongna; Wu, Yaping; Liu, Xinfeng; Sun, Hui; Yang, Rui; Liu, Xin; Chen, Jie; Zhou, Huihui; Ben Ayed, Ismail; Zheng, Hairong

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.