Medical ultrasonic image segmentation method

Abstract

The invention belongs to the technical field of deep learning computer vision and medical information processing, and particularly relates to a medical ultrasonic image segmentation method. Accordingto the method, on the basis of a general image segmentation neural network model, multiple novel technologies such as a multiple-input-multiple-output technology, a hole convolution technology and small sample medical data enhancement are fused, the problems of difficult pain points such as small sample learning, low ultrasonic image contrast and nodule edge blurring are mainly solved, and an optimal segmentation strategy of the method is obtained.

Description

technical field [0001] The invention belongs to the technical field of deep learning computer vision and medical information processing, and in particular relates to a medical ultrasound image segmentation method. Background technique [0002] With the advancement of science and technology, medical imaging technology has made great progress. Ultrasound imaging technology is of great value in preventive diagnosis and treatment due to its advantages of simple operation, no radiation damage, and low cost. At present, the segmentation of regions of interest in medical images is the basis for image analysis and lesion identification. Manual segmentation is widely used clinically to segment ultrasound images, and experienced clinicians manually outline areas of interest based on their professional knowledge. However, manual segmentation is not only time-consuming and extremely dependent on the professional skills and rich experience of doctors, but also due to the blurred edges a...

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Problems solved by technology

However, traditional CNNs cannot reasonably propagate low-level features to high-level

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