Cell segmentation and counting play a very important role in the medical field. The diagnosis of many diseases relies heavily on the kind and number of cells in the blood. convolution neural network achieves encouraging results on image segmentation. However, this data-driven method requires a large number of annotations and can be a time-consuming and expensive process, prone to human error. In this paper, we present a novel frame to segment and count cells without too many manually annotated cell images. Before training, we generated the cell image labels on single-kind cell images using traditional algorithms. These images were then used to form the train set with the label. Different train sets composed of different kinds of cell images are presented to the segmentation model to update its parameters. Finally, the pretrained U-Net model is transferred to segment the mixed cell images using a small dataset of manually labeled mixed cell images. To better evaluate the effectiveness of the proposed method, we design and train a new automatic cell segmentation and count framework. The test results and analyses show that the segmentation and count performance of the framework trained by the proposed method equal the model trained by large amounts of annotated mixed cell images.

Neck lymph node detection is crucial for early cancer metastasis detection and treatment, influencing treatment success and patient survival rates. It also aids in disease staging, monitoring, and treatment selection. It requires the expertise of professional senior radiologists, as the accuracy of current automated detection methods is not sufficiently high. In this study, the neck lymph node detection network (LymoNet) based on YOLOv8 is proposed to detect and classify normal, inflammatory, and metastatic neck lymph nodes from ultrasound images. The advanced attention mechanism modules are utilized to enhance performance of the model, including the Coordinate Attention (CA) which helps the network focus on learning key features in the images, and the Multi-Head Self-Attention (MHSA) which captures global information at different scales. Meanwhile, the medical knowledge embedding which introduces prior knowledge from the medical domain is used to improve the classification performance. By integrating these elements, the YOLOv8 network can achieve better performance in neck lymph node detection tasks. Finally, LymoNet surpassed the benchmark model YOLOv8 by 6.6% in the mAP@.5, achieving the state-of-the-art (SOTA). This model provides a promising solution for automated neck lymph node detection in clinical environments. The proposed methods can also serve as a reference for applying deep learning algorithms in other fields. The source codes, trained weights, and validation data are available on GitHub.

Soft-bodied robots driven by external fields have better environmental adaptability, extending their applications. Nature also provides lots of inspiration for shape-morphing robot development, for example, larvae and jellyfish. This paper presents magnetically propelled hydrogel-based millirobots with volume changeability. The millirobot can be imaged in real time in a completely enclosed space with an ultrasound imaging system. Firstly, a custom-designed magnetic generating system with six square coils was introduced to generate a uniform field to propel the robot. The robot was fabricated using hydrogel with a thickness of around 300 μm. After programmable magnetization, the robot could change its shape and move using the rotating magnetic field. With the near-infrared illumination, the robot could shrink and could recover when the illumination stopped. Even when the robot shrank, it could be propelled by the external field, showing its potential usage in complex environments. Moreover, the posture information of the robot including the position and shape could be obtained in real time using ultrasound image technology.