As a critical technique in transfer learning， domain adaptation addresses the issue of different distributions in training and test datasets well. However， traditional domain adaptation methods are typically limited to scenarios where the target-domain and source-domain datasets are with same number and types of categories. In practical applications， these scenarios are often difficult to meet. Open Set Domain Adaptation （OSDA） emerges to address this challenge. In order to fill the gap in this field and provide a reference for related research， a summary and analysis of OSDA methods emerged in recent years were conducted. Firstly， the related concepts and basic structure were introduced. Secondly， the related typical methods were sorted out and analyzed from three stages： data augmentation-oriented， feature extraction-oriented， and classifier-oriented. Finally， future development directions of OSDA were prospected.

Scan conversion is one of the most important and widely-used technologies in medical ultrasound imaging. Unfortunately, traditional scan conversion algorithm needs intensive computation, which becomes one of the performance bottlenecks of the ultrasound system. In order to overcome this shortcoming, three parallel algorithms called real-time scan conversion for ultrasound based on Compute Unified Device Architecture (CUDA) were proposed. Through assigning the best structure of threads, rationally arranging data transmission between Central Processing Unit (CPU) and Graphic Processing Unit (GPU), and dividing computing tasks, throughput of the algorithm was increased and real-time requirement was met. Finally, this paper compared the three types of real-time scan conversion algorithms on CUDA to traditional method. This paper gets a frame rate of about more than 746fps with the picture size of 3121×936, which is about 300 times faster than the CPU implementation.