Glycine and N-Acetylcysteine (GlyNAC) Combined with Body Weight Support Treadmill Training Improved Spinal Cord and Skeletal Muscle Structure and Function in Rats with Spinal Cord Injury.

Skeletal muscle atrophy is a frequent complication after spinal cord injury (SCI) and can influence the recovery of motor function and metabolism in affected patients. Delaying skeletal muscle atrophy can promote functional recovery in SCI rats. In the present study, we investigated whether a combination of body weight support treadmill training (BWSTT) and glycine and N-acetylcysteine (GlyNAC) could exert neuroprotective effects, promote motor function recovery, and delay skeletal muscle atrophy in rats with SCI, and we assessed the therapeutic effects of the double intervention from both a structural and functional viewpoint.

BEA-Net: Body and Edge Aware Network With Multi-Scale Short-Term Concatenation for Medical Image Segmentation.

Medical image segmentation is indispensable for diagnosis and prognosis of many diseases. To improve the segmentation performance, this study proposes a new 2D body and edge aware network with multi-scale short-term concatenation for medical image segmentation. Multi-scale short-term concatenation modules which concatenate successive convolution layers with different receptive fields, are proposed for capturing multi-scale representations with fewer parameters.

Exploring Contextual Relationships for Cervical Abnormal Cell Detection.

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.

Weakly Supervised Deep Nuclei Segmentation With Sparsely Annotated Bounding Boxes for DNA Image Cytometry.

Nuclei segmentation is an essential step in DNA ploidy analysis by image-based cytometry (DNA-ICM) which is widely used in cytopathology and allows an objective measurement of DNA content (ploidy). The routine fully supervised learning-based method requires often tedious and expensive pixel-wise labels. In this paper, we propose a novel weakly supervised nuclei segmentation framework which exploits only sparsely annotated bounding boxes, without any segmentation labels.

Dual-Branch Network With Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Color Fundus Images.

Automated segmentation of hard exudates in colour fundus images is a challenge task due to issues of extreme class imbalance and enormous size variation. This paper aims to tackle these issues and proposes a dual-branch network with dual-sampling modulated Dice loss. It consists of two branches: large hard exudate biased segmentation branch and small hard exudate biased segmentation branch.

Global context-aware cervical cell detection with soft scale anchor matching.

Background And Objective: Computer-aided cervical cancer screening based on an automated recognition of cervical cells has the potential to significantly reduce error rate and increase productivity compared to manual screening. Traditional methods often rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently, detector based on convolutional neural network is applied to reduce the dependency on hand-crafted features and eliminate the necessary segmentation.

Autofocus of whole slide imaging based on convolution and recurrent neural networks.

During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner.

An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network.

The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditional automatic algorithms often extract the hand-crafted features for recognition. Instead of using the hand-crafted features, in this paper we propose to exploit convolutional neural network (CNN) to learn features in an end-to-end manner to recognize the urinary particle.

Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.

Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminative feature vectors, consisting of local features, morphological features, phase congruency, Hessian and divergence of vector fields, is extracted for each pixel of the fundus image.

Poor lower extremity function was associated with pre-diabetes and diabetes in older chinese people.

Objective: To determine whether a relationship exists between performance-based physical assessments and pre-diabetes/diabetes in an older Chinese population.

Methods: Our study population comprised 976 subjects (mean ± SD age: 67.6±6.