Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

Vulnerable brain regions in adolescent major depressive disorder:A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis

BACKGROUND Adolescent major depressive disorder(MDD)is a significant mental health concern that often leads to recurrent depression in adulthood.Resting-state functional magnetic resonance imaging(rs-fMRI)offers unique insights into the neural mechanisms underlying this condition.However,despite previous research,the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated.AIM To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation(ALE)meta-analysis.METHODS We performed a comprehensive literature search through July 12,2023,for studies investigating brain functional changes in adolescent MDD patients.We utilized regional homogeneity(ReHo),amplitude of low-frequency fluctuations(ALFF)and fractional ALFF(fALFF)analyses.We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls(HCs)using ALE.RESULTS Ten studies(369 adolescent MDD patients and 313 HCs)were included.Combining the ReHo and ALFF/fALFF data,the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs(voxel size:648 mm3,P<0.05),and no brain region exhibited increased activity.Based on the ALFF data,we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients(voxel size:736 mm3,P<0.05),with no regions exhibiting increased activity.CONCLUSION Through ALE meta-analysis,we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients,increasing our understanding of the neuropathology of affected adolescents.

Active hyperspectral imaging with a supercontinuum laser source in the dark

An active hyperspectral imaging(HSI) system was built with a supercontinuum(SC) laser illuminator and a visible/near-infrared hyperspectral camera, which was used for object spectrum detection in the dark. It was demonstrated that the Gaussian-like distribution of the SC illuminator can still be used for accurate reflectance spectrum measurement once the illuminator was characterized in advance. The validity of active HSI results was demonstrated by comparison with passive results. Then, the active HSI system was used to acquire reflectance spectra of different objects in just one pushbroom measurement successfully. With algorithms of principal component analysis clustering and unsupervised K-means spectral classification, this active HSI system with high spectral and spatial resolutions was demonstrated to be efficient and applicable for specific spectrum detections.

Active ferromagnetic shimming of the permanent magnet for magnetic resonance imaging scanner

This paper presents an approach of active ferromagnetic shimming for C-type permanent magnetic resonance imaging （MRI） magnet. It is designed to reduce inhomogeneity of magnetostatic field of C-type permanent magnet to meet the stringent requirement for MRI applications. An optimal configuration （locations and thicknesses） of active ferromagnetic pieces is generated through calculation according to the initial field map and the demanded final homo- geneity specifications. This approach uses a minimisation technique which makes the sum of squared magnetic moment minimum to restrict the amount of the active ferromagnetic material used and the maximal thickness of pieces stacked at each hole location in the shimming hoards. Simulation and experimental results verify that the method is valid and efficient.

Role of serial multiparametric magnetic resonance imaging in prostate cancer active surveillance

AIM:To examine whether addition of 3T multiparametric magnetic resonance imaging(mp MRI)to an active surveillance protocol could detect aggressive or progressive prostate cancer.METHODS:Twenty-three patients with low risk disease were enrolled on this active surveillance study,all of which had Gleason score 6 or less disease.All patients had clinical assessments,including digital rectal examination and prostate specific antigen(PSA)testing,every 6 mo with annual 3T mp MRI scans with gadolinium contrast and minimum sextant prostate biopsies.The MRI images were anonymized of patient identifiers and clinical information and each scan underwentradiological review without the other results known.Descriptive statistics for demographics and follow-up as well as the sensitivity and specificity of mp MRI to identify prostate cancer and progressive disease were calculated.RESULTS:During follow-up(median 24.8 mo)11 of 23 patients with low-risk prostate cancer had disease progression and were taken off study to receive definitive treatment.Disease progression was identified through upstaging of Gleason score on subsequent biopsies for all 11 patients with only 2 patients also having a PSA doubling time of less than 2 years.All 23 patients had biopsy confirmed prostate cancer but only 10 had a positive index of suspicion on mp MRI scans at baseline(43.5% sensitivity).Aggressive disease prediction from baseline mpM RI scans had satisfactory specificity(81.8%)but low sensitivity(58.3%).Twentytwo patients had serial mp MRI scans and evidence of disease progression was seen for 3 patients all of whom had upstaging of Gleason score on biopsy(30% specificity and 100% sensitivity).CONCLUSION:Addition of mp MRI imaging in active surveillance decision making may help in identifying aggressive disease amongst men with indolent prostate cancer earlier than traditional methods.

Minimum variance adaptive beamforming combined with coherence factor weighting applied to ultrafast active cavitation imaging

The ultrafast active cavitation imaging(UACI)based on plane wave transmission and delay-and-sum(DAS)beamforming has been developed to monitor cavitation events with a high frame rate.However,DAS beamforming leads to images with limited resolution and contrast.In this paper,minimum variance(M V)adaptive beamforming and coherence factor(CF)weighting are combined to achieve an MVCF-based UACI,which can improve the cavitation imaging quality.The detailed algorithm evaluation has been investigated from both simulation and experimental data The simulation data include10point targets and a cyst,while the experimental data are obtained by detecting the dissipation of cavitation bubbles in water excited by a single element transducer with frequency of1.2MHz.The advantages of the proposed methodology as well as the comparison with conventional B-mode,DAS?M V,DAS-CF and MV on the basis of compressive sensing(CS)(called MVCS)beamformers are discussed.The results show that MVCF beamformer has a significant improvement in terms of both resolutions and signal-to-noise ratio(SN R).The MVCF-based UACI has a SNR at21.82dB higher,lateral and axial resolution at2.69times and1.93times?respectively,which were compared with those of B-mode active cavitation mapping.The MVCF-based UACI can be used to image the residual cavitation bubbles with a higher SNR and better spatial resolution

Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy

Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons： （1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.

Experimental Study of Assessment on Ventricular Activation Origin and Contraction Sequence by Doppler Tissue Imaging

To evaluate the possibility and accuracy of Doppler tissue image (DTI) on assessment of normal and abnormal ventricular activation and contraction sequence, 9 open chest canine hearts were analyzed by acceleration mode, M mode, and spectrum mode DTI. Our results showed that: (1) Acceleration mode DTI could show the origin of activation and conduction sequence on line; (2) M mode DTI revealed that the activation in mid interventricular septum was earlier than that in mid left ventricular posterior wall at sinus activation; (3) Spectrum DTI showed the ventricular endocardium was activated earlier than the ventricular epicardium in all segments at sinus rhythm. The earliest site of activation of the normal ventricular wall was at middle interventricular septum; the latest site was at basal posterior wall; the contraction sequence was different at the different walls; (4) During abnormal ventricular activation, mid left ventricular posterior wall was activated earliest in accordance with the pacing sites. Abnormal ventricular activation was slower than sinus activation, and the contraction sequence varied at different sites of ventricular wall. It is concluded that DTI can be used to localize the origin of normal or abnormal myocardial activation and to assess the contraction sequence conveniently, accurately and non invasively.

Abnormal brain activity in adolescents with Internet addiction who attempt suicide: an assessment using functional magnetic resonance imaging

Internet addiction is associated with an increased risk of suicidal behavior and can lead to brain dysfunction among adolescents.However,whether brain dysfunction occurs in adolescents with Internet addiction who attempt suicide remains unknown.This observational cross-sectional study enrolled 41 young Internet addicts,aged from 15 to 20 years,from the Department of Psychiatry,the First Affiliated Hospital of Chongqing Medical University,China from January to May 2018.The participants included 21 individuals who attempted suicide and 20 individuals with Internet addiction without a suicidal attempt history.Brain images in the resting state were obtained by a 3.0 T magnetic resonance imaging scanner.The results showed that activity in the gyrus frontalis inferior of the right pars triangularis and the right pars opercularis was significantly increased in the suicidal attempt group compared with the non-suicidal attempt group.In the resting state,the prefrontal lobe of adolescents who had attempted suicide because of Internet addiction exhibited functional abnormalities,which may provide a new basis for studying suicide pathogenesis in Internet addicts.The study was authorized by the Ethics Committee of Chongqing Medical University,China(approval No.2017 Scientific Research Ethics(2017-157))on December 11,2017.

Near-field 3D imaging approach combining MJSR and FGG-NUFFT

A near-field three-dimensional(3 D)imaging method combining multichannel joint sparse recovery(MJSR)and fast Gaussian gridding nonuniform fast Fourier transform(FGGNUFFT)is proposed,based on a perfect combination of the compressed sensing(CS)theory and the matched filtering(MF)technique.The approach has the advantages of high precision and high efficiency:multichannel joint sparse constraint is adopted to improve the problem that the images recovered by the single channel imaging algorithms do not necessarily share the same positions of the scattering centers;the CS dictionary is constructed by combining MF and FGG-NUFFT,so as to improve the imaging efficiency and memory requirement.Firstly,a near-field 3 D imaging model of joint sparse recovery is constructed by combining the MF-based imaging method.Secondly,FGG-NUFFT and reverse FGG-NUFFT are used to replace the interpolation and Fourier transform in MF-based imaging methods,and a sensing matrix with high precision and high efficiency is constructed according to the traditional imaging process.Thirdly,a fast imaging recovery is performed by using the improved separable surrogate functionals(SSF)optimization algorithm,only with matrix and vector multiplication.Finally,a 3 D imagery of the near-field target is obtained by using both the horizontal and the pitching interferometric phase information.This paper contains two imaging models,the only difference is the sub-aperture method used in inverse synthetic aperture radar(ISAR)imaging.Compared to traditional CS-based imaging methods,the proposed method includes both forward transform and inverse transform in each iteration,which improves the quality of reconstruction.The experimental results show that,the proposed method improves the imaging accuracy by about O(10),accelerates the imaging speed by five times and reduces the memory usage by about O(10~2).

High performance active image sensor pixel design with circular structure oxide TFT

We report a high-performance active image sensor pixel design by utilizing amorphous-indium-gallium-zinc-oxide(aIGZO) thin-film transistors(TFTs) with a circular structure. The TFT, configured with the inner electrode as source and outer electrode as drain, typically exhibits good saturation electrical characteristics, where the device has a constant drive current despite variations in drain voltage. Due to the very high output resistance exhibited by this asymmetric TFT structure with a circular shape, the pixel circuit considered here in common-drain configuration provides a higher gain of operation than a pixel circuit implemented with rectangular a-IGZO TFTs. They can be used as driving TFTs in active image sensor circuits. They are, therefore,good candidates for digital X-ray detectors in applications such as medical diagnostic procedures.

Physical Activity, Mediterranean Diet and Biomarkers-Assessed Risk of Alzheimer’s: A Multi-Modality Brain Imaging Study

Increased physical activity and higher adherence to a Mediterranean-type diet (MeDi) have been independently associated with reduced risk of Alzheimer’s disease (AD). Their association has not been investigated with the use of biomarkers. This study examines whether, among cognitively normal (NL) individuals, those who are less physically active and show lower MeDi adherence have brain biomarker abnormalities consistent with AD. Methods: Forty-five NL individuals (age 54 ± 11, 71% women) with complete leisure time physical activity (LTA), dietary information, and cross-sectional 3D T1-weigthed MRI, 11C-Pittsburgh Compound B (PiB) and 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) scans were examined. Voxel-wise multivariate partial least square (PLS) regression was used to examine the effects of LTA, MeDi and their interaction on brain biomarkers. Age, gender, ethnicity, education, caloric intake, BMI, family history of AD, Apolipoprotein E (APOE) genotype, presence of hypertension and insulin resistance were examined as confounds. Subjects were dichotomized into more and less physically active (LTA+ vs. LTA-;n = 21 vs. 24), and into higher vs. lower MeDi adherence groups (n = 18 vs. 27) using published scoring methods. Spatial patterns of brain biomarkers that represented the optimal association between the images and the groups were generated for all modalities using voxel-wise multivariate Partial Least Squares (PLS) regression. Results: Groups were comparable for clinical and neuropsychological measures. Independent effects of LTA and MeDi factors were observed in AD-vulnerable brain regions for all modalities (p β load and lower glucose metabolism) were observed in LTA- compared to LTA+ subjects, and in MeDi- as compared to MeDi+ subjects. A gradient effect was observed for all modalities so that LTA+/MeDi+ subjects had the highest and LTA+/MeDi+ subjects had the lowest AD-burden (p < 0.001), although the LTA × MeDi interaction was significant only for FDG measures (p < 0.03). Adjusting for covariates did not attenuate these relationships. Conclusion: Lower physical activity and MeDi adherence were associated with increased brain AD-burden among NL individuals, in-dicating that lifestyle factors may modulate AD risk. Studies with larger samples and longitudinal evaluations are needed to determine the predictive power of the observed associations.

Geometric active contour based approach for segmentation of high-resolution spaceborne SAR images

Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.

Aging effects of regional activation in a spatial task A functional magnetic resonance imaging study

BACKGROUND： An increasing number of studies have shown the effects of aging in basic cognitive processing and higher cognitive functions using functional magnetic resonance imaging （fMRI）. However, little is known about the aging effects in diverse cognitive abilities, such as spatial learning and reasoning. OBJECTIVE： To investigate the effect of aging on spatial cognitive performance and regional brain activation based on fMRI. DESIGN, TIME, AND SETTING： A block design for fMRI observation. This study was performed at the fMRI Laboratory, Brain Science Research Center, Korea Advanced Institute of Science and Technology from March 2006 to May 2009. PARTICIPANTS： Eight right-handed, male, college students in their 20s （mean age 21.5 years） and six right-handed, male, adults in their 40s （mean age 45.7 years）, who graduated from college, participated in the study. All subjects were healthy and had no prior history of psychiatric or neurological disorders. METHODS： A spatial task was presented while brain images were acquired using a 3T fMRI system （ISOL Technology, Korea）. The spatial tasks involved selecting a shape that corresponded to a given figure using four examples, as well as selecting a development figure of a diagram. MAIN OUTCOME MEASURES： The accuracy rate （number of correct answers/total number of items x 100%） of spatial tasks was calculated. Using the subtraction procedure, the activated areas in the brain during spatial tasks were color-coded by T-score. The double subtraction method was used to analyze the effect of aging between the two age groups （20s versus 40s）. RESULTS： The cerebellum, occipital lobe, parietal lobe, and frontal lobe were similarly activated in the two age groups. Increased brain activations, however, were observed in bilateral parietal and superior frontal lobes of the younger group. More activation was observed in bilateral middle frontal and right inferior frontal lobes in the older group. Compared with the older group, the younger men exhibited greater spatial performance （P = 0.012）. CONCLUSION： Reduced cognitive function correlated with decreased activation areas in the parietal lobe and altered activation in the frontal lobe.

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effectiveness of ALFA-Mix+.

Activation of brain areas following ankle dorsiflexion versus plantar flexion Functional magnetic resonance imaging verification

Changes in activated areas of the brain during ankle active dorsiflexion and ankle active plantar flexion were observed in six healthy subjects using functional magnetic resonance imaging. Excited areas of ankle active dorsiflexion involved the bilateral primary motor area and the primary somatosensory area, as well as the bilateral supplementary sensory area, the primary visual area, the right second visual area, and the vermis of cerebellum. Excited areas of ankle active plantar flexion included the ipsilateral supplementary motor area, the limbic system, and the contralateral corpus striatum. Fine movements of the cerebral cortex control the function of the ankle dorsiflexion to a larger extent than ankle plate flexion, and the function of ankle plate flexion is more controlled by the subcortical area.

Diffusion-weighted magnetic resonance imaging reflects activation of signal transducer and activator of transcription 3 during focal cerebral ischemia/reperfusion

Signal transducer and activator of transcription（STAT）is a unique protein family that binds to DNA,coupled with tyrosine phosphorylation signaling pathways,acting as a transcriptional regulator to mediate a variety of biological effects.Cerebral ischemia and reperfusion can activate STATs signaling pathway,but no studies have confirmed whether STAT activation can be verified by diffusion-weighted magnetic resonance imaging（DWI）in rats after cerebral ischemia/reperfusion.Here,we established a rat model of focal cerebral ischemia injury using the modified Longa method.DWI revealed hyperintensity in parts of the left hemisphere before reperfusion and a low apparent diffusion coefficient.STAT3 protein expression showed no significant change after reperfusion,but phosphorylated STAT3 expression began to increase after 30 minutes of reperfusion and peaked at 24 hours.Pearson correlation analysis showed that STAT3 activation was correlated positively with the relative apparent diffusion coefficient and negatively with the DWI abnormal signal area.These results indicate that DWI is a reliable representation of the infarct area and reflects STAT phosphorylation in rat brain following focal cerebral ischemia/reperfusion.

Fast Image Segmentation Based on a Two-Stage Geometrical Active Contour

A fast two-stage geometric active contour algorithm for image segmentation is developed. First, the Eikonal equation problem is quickly solved using an improved fast sweeping method, and a criterion of local minimum of area gradient (LMAG) is presented to extract the optimal arrival time. Then, the final time function is passed as an initial state to an area and length minimizing flow model, which adjusts the interface more accurately and prevents it from leaking. For object with complete and salient edge, using the first stage only is able to obtain an ideal result, and this results in a time complexity of O(M), where M is the number of points in each coordinate direction. Both stages are needed for convoluted shapes, but the computation cost can be drastically reduced. Efficiency of the algorithm is verified in segmentation experiments of real images with different feature.

Approach for ISAR imaging of near-field targets based on coordinate conversion and image interpolation

Inverse synthetic aperture radar(ISAR)imaging of near-field targets is potentially useful in some specific applications,which makes it very important to efficiently produce highquality image of the near-field target.In this paper,the simplified target model with uniform linear motion is applied to the near-field target imaging,which overcomes the complexity of the traditional near-field imaging algorithm.According to this signal model,the method based on coordinate conversion and image interpolation combined with the range-Doppler(R-D)algorithm is proposed to correct the near-field distortion problem.Compared with the back-projection(BP)algorithm,the proposed method produces better focused ISAR images of the near-field target,and decreases the computation complexity significantly.Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.