Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR. However, there are several types of DR with varying sizes and high inter-class similarity, making segmentation difficult. In this paper, we propose a supervised segmentation method (MSLF-Net) based on multi-scale–multi-level feature fusion to achieve accurate end-to-end DR lesion segmentation. MSLF-Net builds a Multi-Scale Feature Extraction (MSFE) module to extract multi-scale information and provide more comprehensive features for segmentation. This paper further introduces the Multi-Level Feature Fusion (MLFF) module to improve feature fusion using a cross-layer structure. This structure only fuses low- and high-level features of the same class based on category supervision, avoiding feature contamination. Moreover, this paper produces additional masked images for the dataset and performs image enhancement operations to ensure that the proposed method is trainable and functional on small datasets. The extensive experiments are conducted on public datasets IDRID and e_ophtha. The results showed that our proposed feature enhancement method can perform feature fusion more effectively. Therefore, In the end-to-end DR segmentation neural network model, MSLF Net is superior to other similar models in segmentation, and can effectively improve the DR lesion segmentation performance.

The significance of telerobotic trajectory segmentation has been demonstrated on a range of skill training and robotic automation tasks. However, the trajectories of telerobot are characterized by complexity and high dimensionality, making it difficult to segment them accurately. Existing methods are often plagued by feature inefficiency, and the clustering methods are also difficult to properly model the trajectories. In addition, over-segmentation also affects the accuracy of the clustering-based segmentation methods. To address above problems, this article presents a new unsupervised approach that automatically segments multimodal trajectory data of a telerobot and solves the problem of over-segmentation through a postpromoting procedure. First, we present an unsupervised visual feature-extraction network, that is, a dense connection spatial convolution network (DCSC) to generate more discriminative features for clustering. The dense convolution and spatial convolution facilitates information flow, enhances feature propagation, and avoids manual annotation. Next, we develop an unsupervised trajectory segmentation method that is called multimodality clustering with Chinese restaurant process (MC-CRP). The model utilizes data from different modalities and segments the trajectory through hierarchical clustering. MC-CRP obtains more accurate results in a short period of time. To further improve the precision of trajectory segmentation, we merge over-segments based on predefined similarity measurements. Extensive experiments on the publicly available data set JIGSAWS show that the presented approach achieves 70.1% silhouette coefficient, 25.1% normalized mutual information, and 71.4% segmentation accuracy. These metrics demonstrate that the presented segmentation approach provides deeper insight into the trajectory features and improve the accuracy of segmentation more efficiently than others.