利用融合策略和深度学习实现人胎盘的自动3D多模态超声分割
Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning
AI总结：
针对胎盘超声手动或半自动分割耗时且易错的问题，提出结合深度学习与多模态融合策略的3D自动分割方法，数据层融合B模式与能量多普勒效果最佳。
摘要：
目的：超声是临床实践中最常用于诊断和筛查的医学成像模态。由于其安全性、无创性和便携性，超声是妊娠期胎儿评估的主要成像模态。当前的超声处理方法要么是手动的，要么是半自动的，因此费力、耗时且容易出错，而自动化将大大有助于应对这些挑战。在妊娠早期自动识别胎盘变化，可促进针对胎儿生长受限和先兆子痫等疾病的潜在治疗，这些疾病目前仅在妊娠晚期才被发现，从而有可能预防围产期发病率和死亡率。方法：我们提出利用深度学习结合不同融合策略，对人胎盘进行自动三维多模态（B模式和能量多普勒）超声分割。我们收集了包含400项研究的B模式和能量多普勒超声扫描数据。结果：我们基于标准的重叠和边界指标，评估了用于胎盘分割的不同融合策略和最先进的图像分割网络。我们发现，以B模式和能量多普勒扫描形式呈现的多模态信息优于任何单一模态。此外，我们发现，在数据层融合的B模式和能量多普勒输入扫描取得了最佳结果，平均Dice相似系数（DSC）为0.849。结论：我们认为，结合B模式和能量多普勒扫描的多模态方法能够以完全自动化的方式从三维超声扫描中有效分割胎盘，并且对数据集的质量变化具有鲁棒性。
英文摘要：
Purpose: Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice. Due to its safety profile, noninvasive nature and portability, ultrasound is the primary imaging modality for fetal assessment in pregnancy. Current ultrasound processing methods are either manual or semi-automatic and are therefore laborious, time-consuming and prone to errors, and automation would go a long way in addressing these challenges. Automated identification of placental changes at earlier gestation could facilitate potential therapies for conditions such as fetal growth restriction and pre-eclampsia that are currently detected only at late gestational age, potentially preventing perinatal morbidity and mortality. Methods: We propose an automatic three-dimensional multi-modal (B-mode and power Doppler) ultrasound segmentation of the human placenta using deep learning combined with different fusion strategies.We collected data containing Bmode and power Doppler ultrasound scans for 400 studies. Results: We evaluated different fusion strategies and state-of-the-art image segmentation networks for placenta segmentation based on standard overlap- and boundary-based metrics. We found that multimodal information in the form of B-mode and power Doppler scans outperform any single modality. Furthermore, we found that B-mode and power Doppler input scans fused at the data level provide the best results with a mean Dice Similarity Coefficient (DSC) of 0.849. Conclusion: We conclude that the multi-modal approach of combining B-mode and power Doppler scans is effective in segmenting the placenta from 3D ultrasound scans in a fully automated manner and is robust to quality variation of the datasets.
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