ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.

Joint Deep Matching Model of OCT Retinal Layer Segmentation

Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic information.Measuring retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular conditions.Among the existing retinal layer segmentation approaches,learning or deep learning-based methods belong to the state-of-art.However,most of these techniques rely on manual-marked layers and the performances are limited due to the image quality.In order to overcome this limitation,we build a framework based on gray value curve matching,which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from OCT.The depth convolution network learns the column correspondence in the OCT image unsupervised.The whole OCT image participates in the depth convolution neural network operation,compares the gray value of each column,and matches the gray value sequence of the transformation column and the next column.Using this algorithm,when a boundary point is manually specified,we can accurately segment the boundary between retinal layers.Our experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.

人工智能辅助CT血管成像脑血管重建在基层医院颅内动脉瘤诊断中的应用

目的探讨人工智能辅助CT血管成像(CTA)在西藏地区颅内动脉瘤诊治中的应用价值。方法回顾性分析2021年8月至2022年4月拉萨市人民医院神经外科收治的26例颅内动脉瘤患者。所有患者均于24 h内行CTA检查,数据分别使用人工智能辅助和人工方法进行脑血管三维重建,比较2种方法的重建时间、诊断结果、图像质量。采用独立样本t检验比较人工智能重建组和人工重建组重建时间和图像评分的差异,采用χ^(2)检验比较疾病诊断准确性的差异。结果人工智能重建组动脉瘤位置诊断准确性为92.3%(24/26),人工重建组准确性为96.2%(25/26),2组差异无统计学意义(P>0.05)。人工智能重建组CTA重建所需时间显著低于人工重建组[(24.2±11.8)min vs(94.7±42.0)min],差异具有统计学意义(t=-8.82,P<0.001)。人工智能重建组图像评分高于人工重建组[(4.53±0.58)分vs(3.46±0.94)分],差异具有统计学意义(t=4.24,P<0.001)。结论人工智能辅助CTA脑血管重建成像技术较人工重建更快速,显示动脉瘤情况更满意,适合在基层医院应用。

SE-COTR:A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard

Because of the unstructured characteristics of natural orchards,the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture.Therefore,an innovative fruit segmentation method based on deep learning,termed SE-COTR(segmentation based on coordinate transformer),is proposed to achieve accurate and real-time segmentation of green apples.The lightweight network MobileNetV2 is used as the backbone,combined with the constructed coordinate attention-based coordinate transformer module to enhance the focus on effective features.In addition,joint pyramid upsampling module is optimized for integrating multiscale features,making the model suitable for the detection and segmentation of target fruits with different sizes.Finally,in combination with the outputs of the function heads,the dynamic convolution operation is applied to predict the instance mask.In complex orchard environment with variable conditions,SE-COTR achieves a mean average precision of 61.6%with low complexity for green apple fruit segmentation at severe occlusion and different fruit scales.Especially,the segmentation accuracy for small target fruits reaches 43.3%,which is obviously better than other advanced segmentation models and realizes good recognition results.The proposed method effectively solves the problem of low accuracy and overly complex fruit segmentation models with the same color as the background and can be built in portable mobile devices to undertake accurate and efficient agricultural works in complex orchard.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease:diagnosis,longitudinal progress and biological basis

Hippocampal morphological change is one of the main hallmarks of Alzheimer’s disease(AD).However,whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment(MCI)to AD dementia and whether these features provide any neurobiological foundation remains unclear.The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging(MRI)markers for AD.Multivariate classifier-based support vector machine(SVM)analysis provided individual-level predictions for distinguishing AD patients(n=261)from normal controls(NCs;n=231)with an accuracy of 88.21%and intersite crossvalidation.Further analyses of a large,independent the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset(n=1228)reinforced these findings.In MCI groups,a systemic analysis demonstrated that the identified features were significantly associated with clinical features(e.g.,apolipoprotein E(APOE)genotype,polygenic risk scores,cerebrospinal fluid(CSF)Ab,CSF Tau),and longitudinal changes in cognition ability;more importantly,the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI,and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus.The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

Preprocessing method of night vision image application in apple harvesting robot

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for commercialization.The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation mode.Due to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting robot.Three different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be captured.In addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise characteristics.The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise.The preprocessing method can provide a theoretical and technical reference for subsequent image processing.

BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment

Despite of significant achievements made in the detection of target fruits,small fruit detection remains a great challenge,especially for immature small green fruits with a few pixels.The closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard environment.In this paper,we propose a balanced feature pyramid network(BFP Net)for small apple detection.

SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard

Because of the unstructured characteristics of natural orchards,the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture.Therefore,an innovative fruit segmentation method based on deep learning,termed SE-COTR(segmentation based on coordinate transformer),is proposed to achieve accurate and real-time segmentation of green apples.