During image segmentation tasks in computer vision, achieving high accuracy performance while requiring fewer computations and faster inference is a big challenge. This is especially important in medical imaging tasks but one metric is usually compromised for the other. To address this problem, this paper presents an extremely fast, small and computationally effective deep neural network called Stripped-Down UNet (SD-UNet), designed for the segmentation of biomedical data on devices with limited computational resources. By making use of depthwise separable convolutions in the entire network, we design a lightweight deep convolutional neural network architecture inspired by the widely adapted U-Net model. In order to recover the expected performance degradation in the process, we introduce a weight standardization algorithm with the group normalization method. We demonstrate that SD-UNet has three major advantages including: (i) smaller model size (23x smaller than U-Net); (ii) 8x fewer parameters; and (iii) faster inference time with a computational complexity lower than 8M floating point operations (FLOPs). Experiments on the benchmark dataset of the Internatioanl Symposium on Biomedical Imaging (ISBI) challenge for segmentation of neuronal structures in electron microscopic (EM) stacks and the Medical Segmentation Decathlon (MSD) challenge brain tumor segmentation (BRATs) dataset show that the proposed model achieves comparable and sometimes better results compared to the current state-of-the-art.

Gestures, as a basic human feature, occupy an important position in human-computer interaction and other fields as well. In order to accurately recognize gestures and eliminate environmental interference, this paper proposes a gesture recognition matching method based on dynamic bones. This method uses the Mask R-CNN model and exponential filtering to identify and calibrate the key points of the hand. Through the segmentation and feature extraction of real-time frame images, the combined network is used to obtain stable and accurate hand bone key points. Based on the idea of constructing a Spatio-temporal graph convolutional network model based on ST-GCN, a skeletal point information gesture data set is constructed and sent to the network for training. Finally, template matching is used to realize gesture recognition. The experimental results show that the method can eliminate the environmental interference to the greatest extent, as well as the incomplete traditional data set, and the model accuracy defects caused by the lack of special samples. The recognition accuracy of the Chinese sign language database can reach 87.02%. Compared with previous researches on gesture recognition, it has improved accuracy and robustness.