Dear Colleagues,

Medical imaging plays a crucial role in the diagnosis and treatment of various diseases. Ultrasound imaging is a non-invasive and widely used medical imaging technique due to its safety, low cost, and real-time imaging capabilities. However, the interpretation of ultrasound images is challenging due to the low signal-to-noise ratio, artifacts, and variability in image quality and appearance. In recent years, artificial intelligence (AI) has shown great potential in improving the accuracy and efficiency of ultrasound image analysis.

One of the key challenges in ultrasound image analysis is registration, which involves aligning multiple images acquired from different perspectives or at different times. AI-based registration techniques have been developed to overcome this challenge, including deep learning-based registration and feature-based registration. These techniques have demonstrated high accuracy and robustness in various applications, such as cardiac imaging and fetal ultrasound.

Another important task in ultrasound image analysis is segmentation, which involves identifying and delineating regions of interest in the image. AI-based segmentation techniques have been developed to automate this process, including deep learning-based segmentation and graph-cut-based segmentation. These techniques have shown promising results in various applications, such as liver and breast tumor segmentation.

In conclusion, advanced techniques in ultrasound image analysis using AI have shown great potential in improving the accuracy and efficiency of ultrasound image interpretation. AI-based registration and segmentation techniques have demonstrated high accuracy and robustness in various applications, which can lead to improved diagnosis and treatment of various diseases.

So, research articles, reviews and studies in this area of study are welcome. We look forward to receiving your contributions.

Dr. Maryam Fazeli

Section editor