Deep Learning for Endometrium Segmentation in Transvaginal Ultrasound: A Systematic Review Towards Receptivity Assessment

Deep learning (DL) has become a transformative approach in medical image analysis, offering superior accuracy and automation in image segmentation tasks. In reproductive imaging, transvaginal ultrasound (TVUS) serves as a crucial modality for evaluating the endometrial condition, which plays a critical role in assessing ovarian health. Although many studies have applied deep learning to the segmentation of pathological endometrial conditions, research focusing on non-pathological endometrium segmentation remains critically limited. This study presents a comprehensive review of deep learning methods for endometrium segmentation in TVUS, with a focus on non-pathological conditions, including endometrial thickness measurement, morphology analysis, and endometrium receptivity assessment. Following PRISMA guidelines, research articles published between 2015 and 2025 were identified from major scientific databases. The selected studies were analyzed in terms of image processing methods, deep learning architectures, and performance metrics, such as Dice coefficient, Jaccard index, precision, recall, and Hausdorff distance. Although foundational architectures, such as U-Net and its variants, achieve impressive Dice coefficients (up to 0.977), the results often rely on small and single-center datasets, proving limited generalizability across imaging settings. Recent advancements demonstrate the efficacy of hybrid architectures, such as the Deep Learned Snake algorithm and Transformer-based models like SAIM, in optimizing segmentation precision within noisy transvaginal ultrasound images. This review highlights the lack of attention to non-pathological endometrium segmentation and guides future research directions in self-supervised learning, transformer-based architectures, and interpretable deep learning to achieve robust and clinically applicable models for enhancing endometrium receptivity assessment and supporting ovarian health in assisted reproduction technology.