OTADiff: Ovarian Tumor-Aware Diffusion Model for Ultrasound Image Augmentation and Detection

Deep learning models typically require large amounts of training data. However, in the medical domain, data annotation is time-consuming and must be performed by domain experts, especially when it involves delineating tumors, such as ovarian masses, in grayscale ultrasound images that are often of low quality. In this study, we propose a diffusion-based data augmentation approach that generates new ultrasound images guided by tumor boundary masks, namely OTADiff. This reduces the manual effort required for detailed region annotation. Ovarian ultra-sound imaging presents two key challenges: i) tumors often resemble surrounding normal tissue, making them difficult to distinguish; ii) ultrasound images typically exhibit signal attenuation, with the lower regions appearing darker than the upper ones. In this paper, we extend the B-Maps approach by additionally addressing the first challenge. Specifically, our method enhances focus on tumor regions to preserve their texture and intensity while downplaying background areas. This results in the generation of tumor-aware synthetic ultrasound images. The generated images are evaluated using the LPIPS metric, and only high-quality samples are retained to enrich the training dataset for detection models. We conduct experiments on two ovarian ultrasound datasets (OUT_2d and OvaTUS) using YOLOv11 and Faster R-CNN detectors. Results show that our tumor-aware augmentation improves image quality and boosts detection mAP by 4% compared to training without augmented data.