Spinal Vertebral Fracture Detection and Fracture Level Assessment Based on Deep Learning

This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’diagnostic efficiency.Therefore,a deep-learning-based automated diagnostic systemwithmulti-label segmentation is proposed to recognize the condition of vertebral fractures.The whole spine Computed Tomography(CT)image is segmented into the fracture,normal,and background using U-Net,and the fracture degree of each vertebra is evaluated(Genant semi-qualitative evaluation).The main work of this paper includes:First,based on the spatial configuration network(SCN)structure,U-Net is used instead of the SCN feature extraction network.The attention mechanismandthe residual connectionbetweenthe convolutional layers are added in the local network(LN)stage.Multiple filtering is added in the global network(GN)stage,and each layer of the LN decoder feature map is filtered separately using dot product,and the filtered features are re-convolved to obtain the GN output heatmap.Second,a network model with improved SCN(M-SCN)helps automatically localize the center-of-mass position of each vertebra,and the voxels around each localized vertebra were clipped,eliminating a large amount of redundant information(e.g.,background and other interfering vertebrae)and keeping the vertebrae to be segmented in the center of the image.Multilabel segmentation of the clipped portion was subsequently performed using U-Net.This paper uses VerSe’19,VerSe’20(using only data containing vertebral fractures),and private data(provided by Guizhou Orthopedic Hospital)for model training and evaluation.Compared with the original SCN network,the M-SCN reduced the prediction error rate by 1.09%and demonstrated the effectiveness of the improvement in ablation experiments.In the vertebral segmentation experiment,the Dice Similarity Coefficient(DSC)index reached 93.50%and the Maximum Symmetry Surface Distance(MSSD)index was 4.962 mm,with accuracy and recall of 95.82%and 91.73%,respectively.Fractured vertebrae were also marked as red and normal vertebrae were marked as white in the experiment,and the semi-qualitative assessment results of Genant were provided,as well as the results of spinal localization visualization and 3D reconstructed views of the spine to analyze the actual predictive ability of the model.It provides a promising tool for vertebral fracture detection.

A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.

Characterization of wet microalgal cells pretreated with steam for lipid extraction

Steam pretreatment was employed to disrupt Microalgal cells for lipids extraction.Effects of steam pretreatment on microstructure of microalgal cells were investigated through scanning electron microscopy(SEM)and transmission electron microscopy(TEM).Effect of treatment on lipid extraction was also studied.Microalgal cell walls were distorted after steam pretreatment due to the hydrolysis of organic macromolecules contained in cell wall.Maximum curvature was increased from 1.88×10^(-6) m^(-1) to 1.43×10^(-7) m^(-1) after treatment with the steam at 130℃.The fractal dimension of microalgal cells increased from 1.25 to 1.30 after pretreatment for 15 min,and further increased to 1.47 when the pretreatment time was increased to 60 min.Increased steam pretreatment temperature and time enhanced the hydrolysis of organic macromolecules,and finally destroyed microalgal cell walls at pretreatment temperature of 130℃and pretreatment time of 60 min.Lipid extracted from wet microalgal was significantly increased(2.1-fold)after pretreatment.