Weakly-supervised ultrasound image segmentation with elliptical shape prior constraint

Accurate pixel-level segmentation of ultrasound (US) images is vital for computer-aided disease screening, diagnosis, and treatment response evaluation. The weakly supervised methods have the potential to reduce the time-consuming and labor-intensive workload for radiologists, paving the way for further automation in the quantitative analysis of US images. Among these methods, the multiple instance learning (MIL) has proven effective and is often applied to prediction tasks with insufficiently labeled data. In US examinations, the elliptical region formed by intersecting lines used by radiologists for target annotation serves as a crucial prior information. Therefore, we propose a novel weakly supervised method called elliptical shape prior constraint MIL (ESPC-MIL) for pixel-level segmentation of US images. ESPC-MIL incorporates an elliptical shape prior constraint into the MIL framework, delivering more accurate foreground and background candidate regions for MIL, which enhances its predictive performance for tissues and organs with approximately elliptical shapes. Furthermore, the method utilizes elliptical shape prior information for global supervision, improving edge segmentation and localization accuracy. Compared to other weakly supervised methods, ESPC-MIL achieves state-of-the-art results on four US image datasets: Achilles tendon dataset, median nerve dataset, private breast tumor dataset, and public breast ultrasound image dataset, with Dice similarity coefficients of 0.855, 0.849, 0.876, and 0.748, respectively. It demonstrates performance comparable to fully supervised segmentation methods while significantly reducing annotation requirements. Notably, the method demonstrates a more significant performance improvement in segmenting objects with approximately elliptical shapes compared to those with complex shapes. Source codes and models are available at https://github.com/CYWang-kayla/ESPC-MIL-Model.