Accurate detection and segmentation of anatomical structures from ultrasound
images are crucial for clinical diagnosis and biometric measurements. Although
ultrasound imaging has been widely used with superiorities such as low cost and
portability, the fuzzy border definition and existence of abounding artifacts
pose great challenges for automatically detecting and segmenting the complex
anatomical structures. In this paper, we propose a multi-domain regularized
deep learning method to address this challenging problem. By leveraging the
transfer learning from cross domains, the feature representations are
effectively enhanced. The results are further improved by the iterative
refinement. Moreover, our method is quite efficient by taking advantage of a
fully convolutional network, which is formulated as an end-to-end learning
framework of detection and segmentation. Extensive experimental results on a
large-scale database corroborated that our method achieved a superior detection
and segmentation accuracy, outperforming other methods by a significant margin
and demonstrating competitive capability even compared to human performance.

本文提出了一种基于多域正则化深度学习的超声图像解剖结构检测和分割方法，通过跨领域迁移学习和迭代优化等手段提高了特征表达和分类精度，与人类表现相当且优于其他方法。