Speakers
Description
Nowadays, medical images play a mainstay role in medical diagnosis, and computer tomography, nuclear magnetic resonance, ultrasound and other imaging technologies have become a powerful means of in vitro imaging. Extracting lesion information from these images can enable doctors to observe and diagnose the lesion more effectively, so as to improve the accuracy of quasi diagnosis. Therefore, the segmentation of medical images has important social value.The achievement of image semantic segmentation shows the potential of the Convolutional Neural Network (CNN) for medical image analysis. However, the application of the existing CNN model to the video neglect the correlation between frames of the video. A video semantic segmentation framework based on U-Net is proposed in this article that the feature map of the pre-frame is propagated to the next frame via an optical flow field. The accuracy of segmentation is boosted with slight performance degradation. The framework includes three parts: 1) a segmentation sub module using UNet to segment the current frame; 2) an optical flow feature extraction module to perform feature extraction on the motion information of the current frame and the previous frame; 3) a correction module, which assigns weights to the segmentation results and optical flow features to achieve the correction effect. The effectiveness of our proposed method is presented on two public datasets (Drosophila melanogaster electron micrographs, Chaos), and private Digital Subtraction Angiography (DSA) video datasets.
