Res-ECA-UNet++: an automatic segmentation model for ovarian tumor ultrasound images based on residual networks and channel attention mechanism.
Journal:
Frontiers in medicine
Published Date:
May 21, 2025
Abstract
OBJECTIVE: Ultrasound imaging has emerged as the preferred imaging modality for ovarian tumor screening due to its non-invasive nature and real-time dynamic imaging capabilities. However, in many developing countries, ultrasound diagnosis remains dependent on specialist physicians, where the shortage of skilled professionals and the relatively low accuracy of manual diagnoses significantly constrain screening efficiency. Although deep learning has achieved remarkable progress in medical image segmentation in recent years, existing methods still face challenges in ovarian tumor ultrasound segmentation, including insufficient robustness, imprecise boundary delineation, and dependence on high-performance hardware facilities. This study proposes a deep learning-based automatic segmentation model, Res-ECA-UNet++, designed to enhance segmentation accuracy while alleviating the strain on limited healthcare resources.
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