ABSTRACT
In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.
Subject(s)
Humans , Algorithms , Deep Learning , Image Processing, Computer-Assisted/methods , Thyroid Diseases/diagnostic imaging , UltrasonographyABSTRACT
External beam radiation therapy (EBRT) is one of the main treatments for prostate cancer, and image-guided implementation of EBRT is more suitable for accurate radiotherapy. As a new type of image-guided technology, the Clarity system has been applied in the real-time tracking during EBRT for prostate cancer in clinical practice. While improving the accuracy of EBRT targeting, it also significantly reduces the side effects of traditional EBRT. In this article, the application of Clarity system in EBRT of prostate cancer and its existing problems were systematically elucidated.
ABSTRACT
When focusing using an ultrasonic transducer array, a main lobe is formed in the focal region of an ultrasound field, but side lobes also arise around the focal region due to the leakage. Since the side lobes cannot be completely eliminated in the focusing process, they are responsible for subsequent ultrasound image quality degradation. To improve ultrasound image quality, a signal processing strategy to reduce side lobes is definitely in demand. To this end, quantitative determination of main and side lobes is necessary. We propose a theoretically and actually error-free method of exactly discriminating and separately computing the main lobe and side lobe parts in ultrasound image by computer simulation. We refer to images constructed using the main and side lobe signals as the main and side lobe images, respectively. Since the main and side lobe images exactly represent their main and side lobe components, respectively, they can be used to evaluate ultrasound image quality. Defining the average brightness of the main and side lobe images, the conventional to side lobe image ratio, and the main to side lobe image ratio as image quality metrics, we can evaluate image characteristics in speckle images. The proposed method is also applied in assessing the performance of side lobe suppression filtering. We show that the proposed method may greatly aid in the evaluation of medical ultrasonic images using computer simulations, albeit lacking the use of actual experimental data.
Subject(s)
Computer Simulation , Methods , Transducers , Ultrasonics , UltrasonographyABSTRACT
Objective:Give a method for tracing boundary of inner-medium membrane in the ultrasonic image of common carotid artery.Methods: Giving a cost function before the programme begins to search the pixel point set whose cost function have the minimum value. The searching process starts from an initial anchor point given by the precedent programme. The calculation of cost function on each pixel point column on both sides of the initial anchor point will not stop until the terminal conditions are fulfilled. The programme will record the coordinates and pixel values of all the points with the minimum cost function value obtained from the calculation.Results: It can trace the boundary of inner-medium membrane in the CCA image from a clinical exam and can export the processed image with highlighted area. There is a daemon that records the coordinates and pixel values of the point set.Conclusion: This method can trace the boundary of inner-medium membrane in the CCA ultrasonic image when the CCA image is distinct.
ABSTRACT
Objective To propose an extraction algorithm for extraction of breast tumor boundary in its ultrasonic image. Methods A novel Global-Local Energy function was proposed in the variational Level Set formulation for the curve evolution. By enhancing the intensity of local information, the breast tumor boundary was obtained from ultrasonic images accurately. Results Experiments on a synthetic ultrasound image showed that this method was more accurate at segmenting the object from the background than the Chan-Vese algorithm based on the global optimization. Experiments on 103 ultrasonic breast tumor images also showed that the boundaries were extracted accurately with this method without any pre-processing step. Conclusion This proposed method is expected for the fast and accurate boundary extraction of ultrasonic breast tumor boundary from ultrasonic image.