Deep learning-based methods have shown excellent potential on object detection and pose estimation with vast amounts of training data to achieve good performance. Obtaining enough comprehensive manual labeling training data is a time-consuming and error-prone task in industrial scenes, and most current time-saving synthetic data generation methods require detailed high-quality CAD models. Instead, we introduce an image-to-image translation based synthetic data generation approach, which requires only untextured CAD models and a small number of real images. The proposed approach starts with edge maps extracted by mesh model projection. And each edge map is provided with realistic appearance learned from real images through image-to-image translation to achieve patch-level realism, and then superimposed on randomly selected backgrounds with a simple Cut-and-Paste strategy. Experiments on our custom dataset and T-LESS dataset show that our synthetic data is competitive with uncomprehensive real data and gives a considerable improvement when compared to the same degree of model rendering images. In addition, the proposed approach allows for extensibility to accommodate new parts with similar texture and structure. And it shows the potential to train image translation model on only one single object and synthesize data for multiples in one take.

Objective: Paraspinal muscle segmentation and reconstruction from MR images are critical to implement quantitative assessment of chronic and recurrent low back pains. Due to unclear muscle boundaries and shape variations, current segmentation methods demonstrate suboptimal performance with insufficient training samples. This work proposes a novel approach to modeling and inferring muscle shapes that enhances segmentation accuracy and efficiency with few training data. Methods: Firstly, a probabilistic shape model (PSM) based on Fourier basis functions and Gaussian processes (GPs) is designed to encode 3D muscle shapes, where anatomical meanings are attributed to the model's geometric parameters. Muscle shape variations and correlations are described by the GPs of the geometric parameters, which allow a small size of parameters to model the distribution of muscle shapes. Secondly, a Bayesian framework is developed to achieve entire muscle segmentation by posterior estimations. The framework fuses the geometric prior of the PSM with observations of deep-learning-based edge detections (DED) and sparse manual annotations, by which issues of unclear boundaries and shape variations can be compensated. Results and conclusion: Experiments on public and clinical datasets demonstrate that, with just three manually annotated slices, our method achieves a Dice similarity coefficient exceeding 90%, which outperforms other methods. Meanwhile, our method needs only a small training dataset and offers rapid inference speeds in clinical applications. Significance: Our study enables precise assessment of paraspinal muscles in 2D and 3D, aiding clinicians and researchers in understanding muscle changes in various conditions, potentially enhancing treatment outcomes.

Biological fish exhibit a remarkably broad-spectrum visual perception capability. Inspired by the eye arrangement of biological fish, we design a fish-like binocular vision system, thereby endowing underwater bionic robots with an exceptionally broad visual perception capacity. Firstly, based on the design principles of binocular visual field overlap and tangency to streamlined shapes, a fish-like vision system is developed for underwater robots, enabling wide-field underwater perception without a waterproof cover. Secondly, addressing the significant distortion and parallax of the vision system, a visual field stitching algorithm is proposed to merge the binocular fields of view and obtain a complete perception image. Thirdly, an orientation alignment method is proposed that draws scales for yaw and pitch angles in the stitched images to provide a reference for the orientation of objects of interest within the field of view. Finally, underwater experiments evaluate the perception capabilities of the fish-like vision system, confirming the effectiveness of the visual field stitching algorithm and the orientation alignment method. The results show that the constructed vision system, when used underwater, achieves a horizontal field of view of 306.56°. The conducted work advances the visual perception capabilities of underwater robots and presents a novel approach to and insight for fish-inspired visual systems.

Objective. Ossification of the posterior longitudinal ligament (OPLL) is a prevalent cervical spine degeneration disease leading to significant spinal cord dysfunctions. Due to morphological diversity and data scarcity, traditional OPLL assessment relies on manual measurements, which suffer from low consistency and high cost. To implement automated quantification of the OPLL, a cognition-inspired segmentation framework, named the probabilistic anatomical cognition (PAC) framework, is proposed to encode physicians’ anatomical knowledge of the OPLL and mimic their hierarchical logic of inferring lesions. Approach. The OPLL anatomical structure is firstly modeled by a multi-level probabilistic representation from the stochastic global shape of the spinal canal (SC) to the local feature distributions of the lesions. Based on the anatomical prior model, the OPLL segmentation is implemented by the deep-logic shape inference. The logic extracts high-confidence global feature observations of the SC, following with the inference to the local lesions by morphological correlations. The fusion of the anatomical prior and multi-level observations enhances both interpretability and generalization of lesion segmentation and reduces reliance on large datasets. Main results. Tested on a clinical dataset of 439 patients, the PAC framework improves dice similarity coefficient by 10% over the lightweight baseline and achieves high consistency with expert assessments on clinical lesion metrics. Significance. A general automated segmentation pipeline and three-dimensional metrics are provided for the first time by the framework to quantify the OPLL degeneration, which offers valuable insights to support surgical decision-making.