Deep Learning in Medical Ultrasound Image Segmentation: a Review
It provides a comprehensive overview for researchers and practitioners in medical imaging, but is incremental as it synthesizes existing work without new results.
This review categorizes and analyzes deep learning methods for medical ultrasound image segmentation, summarizing current algorithms, evaluation methods, datasets, and discussing challenges and future directions.
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization. In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first. Secondly, for each group, several current representative algorithms are selected, introduced, analyzed and summarized in detail. In addition, common evaluation methods for image segmentation and ultrasound image segmentation datasets are summarized. Further, the performance of the current methods and their evaluations are reviewed. In the end, the challenges and potential research directions for medical ultrasound image segmentation are discussed.