Jittou Asmaa, Fazazy Khalid El, Riffi Jamal
Laboratory of Computer Science, Innovation, and Artificial Intelligence, Faculty of Science Dhar El Mahraz, University Sidi Mohamed Ben Abdellah, 30000, Fes, Morocco.
Vis Comput Ind Biomed Art. 2025 Jul 15;8(1):17. doi: 10.1186/s42492-025-00197-8.
Placental segmentation is critical for the quantitative analysis of prenatal imaging applications. However, segmenting the placenta using magnetic resonance imaging (MRI) and ultrasound is challenging because of variations in fetal position, dynamic placental development, and image quality. Most segmentation methods define regions of interest with different shapes and intensities, encompassing the entire placenta or specific structures. Recently, deep learning has emerged as a key approach that offer high segmentation performance across diverse datasets. This review focuses on the recent advances in deep learning techniques for placental segmentation in medical imaging, specifically MRI and ultrasound modalities, and cover studies from 2019 to 2024. This review synthesizes recent research, expand knowledge in this innovative area, and highlight the potential of deep learning approaches to significantly enhance prenatal diagnostics. These findings emphasize the importance of selecting appropriate imaging modalities and model architectures tailored to specific clinical scenarios. In addition, integrating both MRI and ultrasound can enhance segmentation performance by leveraging complementary information. This review also discusses the challenges associated with the high costs and limited availability of advanced imaging technologies. It provides insights into the current state of placental segmentation techniques and their implications for improving maternal and fetal health outcomes, underscoring the transformative impact of deep learning on prenatal diagnostics.
胎盘分割对于产前影像应用的定量分析至关重要。然而，由于胎儿位置的变化、胎盘动态发育以及图像质量等因素，使用磁共振成像（MRI）和超声对胎盘进行分割具有挑战性。大多数分割方法通过定义具有不同形状和强度的感兴趣区域来涵盖整个胎盘或特定结构。最近，深度学习已成为一种关键方法，在各种数据集中都具有很高的分割性能。本综述聚焦于医学成像中胎盘分割的深度学习技术的最新进展，特别是MRI和超声模态，并涵盖2019年至2024年的研究。本综述综合了近期研究，拓展了这一创新领域的知识，并强调了深度学习方法在显著增强产前诊断方面的潜力。这些发现强调了选择适合特定临床场景的合适成像模态和模型架构的重要性。此外，整合MRI和超声可以通过利用互补信息来提高分割性能。本综述还讨论了与先进成像技术的高成本和有限可用性相关的挑战。它提供了对胎盘分割技术现状及其对改善母婴健康结果的影响的见解，强调了深度学习对产前诊断的变革性影响。
