Automatic segmentation method using FCN with multi-scale dilated convolution for medical ultrasound image

• Published in: The Visual computer Vol. 39; no. 11; pp. 5953 - 5969
• Main Authors: Qian, Ledan, Huang, Huiling, Xia, Xiaonyu, Li, Yi, Zhou, Xiao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Abdomen; Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Blood vessels; Cancer; Computer Graphics; Computer Science; Datasets; Deep learning; Feature extraction; Image enhancement; Image filters; Image Processing and Computer Vision; Image segmentation; Medical imaging; Medical research; Neural networks; Original Article; Prostate; Qualitative analysis; Research methodology; Semantic segmentation; Semantics; Thyroid gland; Ultrasonic imaging; Ultrasonic testing; Image segmentation; Laplace correction operator; Dilated convolution; Fully convolutional network; Medical ultrasound image

Image segmentation plays a critical role in the quantitative and qualitative analysis of medical ultrasound images, directly affecting the follow-up analysis and processing. However, due to the speckle noise, fuzziness, complexity and diversity of medical ultrasound images, the traditional image seg...