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1.
JMIR Med Inform ; 9(2): e24572, 2021 Feb 11.
Artigo em Inglês | MEDLINE | ID: mdl-33534723

RESUMO

BACKGROUND: COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. OBJECTIVE: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. METHODS: Clinical data-including demographics, signs, symptoms, comorbidities, and blood test results-and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. RESULTS: Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929). CONCLUSIONS: Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.

2.
Neuroradiology ; 2021 Jan 26.
Artigo em Inglês | MEDLINE | ID: mdl-33501512

RESUMO

PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. METHODS: We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model's accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. RESULTS: The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. CONCLUSION: The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients.

3.
JMIR Med Inform ; 8(11): e19805, 2020 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-33200991

RESUMO

BACKGROUND: The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. OBJECTIVE: This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans. METHODS: In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons. RESULTS: DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy). CONCLUSIONS: Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2155-2158, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018433

RESUMO

Exercising has various health benefits and it has become an integral part of the contemporary lifestyle. However, some workouts are complex and require a trainer to demonstrate their steps. Thus, there are various workout video tutorials available online. Having access to these, people are able to independently learn to perform these workouts by imitating the poses of the trainer in the tutorial. However, people may injure themselves if not performing the workout steps accurately. Therefore, previous work suggested to provide visual feedback to users by detecting 2D skeletons of both the trainer and the learner, and then using the detected skeletons for pose accuracy estimation. Using 2D skeletons for comparison may be unreliable, due to the highly variable body shapes, which complicate their alignment and pose accuracy estimation. To address this challenge, we propose to estimate 3D rather than 2D skeletons and then measure the differences between the joint angles of the 3D skeletons. Leveraging recent advancements in deep latent variable models, we are able to estimate 3D skeletons from videos. Furthermore, a positive-definite kernel based on diversity-encouraging prior is introduced to provide a more accurate pose estimation. Experimental results show the superiority of our proposed 3D pose estimation over the state-of-the-art baselines.

5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 998-1001, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018153

RESUMO

Voice command is an important interface between human and technology in healthcare, such as for hands-free control of surgical robots and in patient care technology. Voice command recognition can be cast as a speech classification task, where convolutional neural networks (CNNs) have demonstrated strong performance. CNN is originally an image classification technique and time-frequency representation of speech signals is the most commonly used image-like representation for CNNs. Various types of time-frequency representations are commonly used for this purpose. This work investigates the use of cochleagram, utilizing a gammatone filter which models the frequency selectivity of the human cochlea, as the time-frequency representation of voice commands and input for the CNN classifier. We also explore multi-view CNN as a technique for combining learning from different time-frequency representations. The proposed method is evaluated on a large dataset and shown to achieve high classification accuracy.


Assuntos
Redes Neurais de Computação , Voz , Humanos , Fala
6.
Sci Rep ; 10(1): 7733, 2020 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-32382048

RESUMO

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient's treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients' age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas' IDH status prediction.


Assuntos
Glioma/genética , Isocitrato Desidrogenase/genética , Prognóstico , Adulto , Idoso , Aprendizado Profundo , Feminino , Glioma/diagnóstico por imagem , Glioma/patologia , Humanos , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Mutação/genética
7.
Brain ; 142(2): 426-442, 2019 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-30668642

RESUMO

The spread of neurodegeneration through the human brain network is reported as underlying the progression of neurodegenerative disorders. However, the exact mechanisms remain unknown. The human visual pathway is characterized by its unique hierarchical architecture and, therefore, represents an ideal model to study trans-synaptic degeneration, in contrast to the complexity in neural connectivity of the whole brain. Here we show in two specifically selected patient cohorts, including (i) glaucoma patients with symmetrical bilateral hemifield defects respecting the horizontal meridian (n = 25, 14 females, 64.8 ± 10.1 years; versus 13 normal controls with similar age/sex distributions); and (ii) multiple sclerosis patients without optic radiation lesions (to avoid potential effects of lesions on diffusivity measures) (n = 30, 25 females, 37.9 ± 10.8 years; versus 20 controls), that there are measurable topographic changes in the posterior visual pathways corresponding to the primary optic nerve defects. A significant anisotropic increase of water diffusion was detected in both patient cohorts in the optic radiations, characterized by changes in perpendicular (radial) diffusivity (a measure of myelin integrity) that extended more posteriorly than those observed in parallel (axial) diffusivity (reflecting axonal integrity). In glaucoma, which is not considered a demyelinating disease, the observed increase in radial diffusivity within the optic radiations was validated by topographically linked delay of visual evoked potential latency, a functional measure of demyelination. Radial diffusivity change in the optic radiations was also associated with an asymmetrical reduction in the thickness of the calcarine cortex in glaucoma. In addition, 3 years longitudinal observation of the multiple sclerosis patient cohort revealed an anterograde increase of radial diffusivity in the anterior part of optic radiations which again was retinotopically associated with the primary damage caused by optic neuritis. Finally, in an animal model of optic nerve injury, we observed early glial activation and demyelination in the posterior visual projections, evidenced by the presence of myelin-laden macrophages. This occurred prior to the appearance of amyloid precursor protein accumulation, an indicator of disrupted fast axonal transport. This study demonstrated strong topographical spread of neurodegeneration along recognized neural projections and showed that myelin and glial pathology precedes axonal loss in the process, suggesting that the mechanism of trans-synaptic damage may be at least partially mediated by glial components at the cellular level. The findings may have broad biological and therapeutic implications for other neurodegenerative disorders.


Assuntos
Axônios/patologia , Doenças Desmielinizantes/diagnóstico por imagem , Doenças Neurodegenerativas/diagnóstico por imagem , Neurônios/patologia , Adulto , Idoso , Animais , Axônios/fisiologia , Estudos de Coortes , Doenças Desmielinizantes/fisiopatologia , Feminino , Humanos , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Pessoa de Meia-Idade , Doenças Neurodegenerativas/fisiopatologia , Neurônios/fisiologia
8.
Ophthalmology ; 126(3): 445-453, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30060979

RESUMO

PURPOSE: To assess differential patterns of axonal loss and demyelination in the optic nerve in multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD). DESIGN: Cross-sectional study. PARTICIPANTS: One hundred ninety-two participants, including 136 MS patients (272 eyes), 19 NMOSD patients (38 eyes), and 37 healthy control participants (74 eyes). METHODS: All participants underwent spectral-domain OCT scans and multifocal visual evoked potential (mfVEP) recordings. High-resolution magnetic resonance imaging (MRI) with the diffusion protocol also was performed in all patients. MAIN OUTCOME MEASURES: Ganglion cell-inner plexiform layer (GCIPL) thickness and mfVEP amplitude and latency at 5 eccentricities; global and temporal retinal nerve fiber layer thickness. RESULTS: In optic neuritis (ON) eyes, the NMOSD patients had more severe GCIPL loss (P < 0.001) and mfVEP amplitude reduction (P < 0.001) compared with MS patients, whereas in contrast, mfVEP latency delay was more evident in MS patients (P < 0.001). The NMOSD patients showed more morphologic and functional loss at the foveal to parafoveal region, whereas the MS patients showed evenly distributed damage at the macula. Correlation analysis demonstrated a strong structure-function (OCT-mfVEP) association in the NMOSD patients, which was only moderate in the MS patients. In non-ON (NON) eyes, the MS patients showed significantly thinner GCIPL than controls (P < 0.001), whereas no GCIPL loss was observed in NON eyes in NMOSD. In addition, a significant correlation was found between all OCT and mfVEP measures in MS patients, but not in NMOSD patients. MRI demonstrated significant lesional load in the optic radiation in MS compared to NMOSD eyes (P = 0.002), which was related to the above OCT and mfVEP changes in NON eyes. CONCLUSIONS: Our study demonstrated different patterns of ON damage in NMOSD and MS. In MS, the ON damage was less severe, with demyelination as the main pathologic component, whereas in NMOSD, axonal loss was more severe compared with myelin loss. The disproportional mfVEP amplitude and latency changes suggested predominant axonal damage within the anterior visual pathway as the main clinical feature of NMOSD, in contrast to MS, where demyelination spreads along the entire visual pathway.


Assuntos
Potenciais Evocados Visuais/fisiologia , Esclerose Múltipla/fisiopatologia , Neuromielite Óptica/fisiopatologia , Nervo Óptico/fisiopatologia , Neurite Óptica/fisiopatologia , Adulto , Axônios/patologia , Estudos Transversais , Feminino , Humanos , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Esclerose Múltipla/diagnóstico por imagem , Neuromielite Óptica/diagnóstico por imagem , Neurite Óptica/diagnóstico por imagem , Células Ganglionares da Retina/patologia , Tomografia de Coerência Óptica/métodos , Acuidade Visual , Vias Visuais/fisiopatologia
9.
Neuroimage Clin ; 17: 1028-1035, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29387524

RESUMO

Objective: Using diffusion tensor imaging (DTI), we examined chronic stable MS lesions, peri-lesional white matter (PLWM) and normal appearing white matter (NAWM) in patients with relapsing-remitting multiple sclerosis (RRMS) for evidence of progressive tissue destruction and evaluated whether diffusivity change is associated with conventional MRI parameters and clinical findings. Method: Pre- and post-gadolinium T1, T2 and DTI images were acquired from 55 consecutive RRMS patients at baseline and 42.3 ± 9.7 months later. Chronic stable T2 lesions of sufficient size were identified in 43 patients (total of 134 lesions). Diffusivity parameters such as axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD) and fractional anisotropy (FA) were compared at baseline and follow-up. MRI was also performed in 20 normal subjects of similar age and gender. Results: Within the core of chronic MS lesions the diffusion of water molecules significantly increased over the follow-up period, while in NAWM all diffusivity indices remained stable. Since increase of AD and RD in lesional core was highly concordant, indicating isotropic nature of diffusivity change, and considering potential effect of crossing fibers on directionally-selective indices, only MD, a directionally-independent measure, was used for further analysis. The significant increase of MD in the lesion core during the follow-up period (1.29 ± 0.19 µm2/ms and 1.34 ± 0.20 µm2/ms at baseline and follow-up respectively, P < 0.0001) was independent of age or disease duration, total brain lesion volume or new lesion activity, lesion size or location and baseline tissue damage (T1 hypointensity). Change of MD in the lesion core, however, was associated with progressive brain atrophy (r = 0.47, P = 0.002). A significant gender difference was also observed: the MD change in male patients was almost twice that of female patients (0.030 ± 0.04 µm2/ms and 0.058 ± 0.03 µm2/ms in female and male respectively, P = 0.01). Sub-analysis of lesions with lesion-free surrounding revealed the largest MD increase in the lesion core, while MD progression gradually declined towards PLWM. MD in NAWM remained stable over the follow-up period. Conclusion: The significant increase of isotropic water diffusion in the core of chronic stable MS lesions likely reflects gradual, self-sustained tissue destruction in demyelinated white matter that is more aggressive in males.


Assuntos
Encéfalo/diagnóstico por imagem , Imagem de Tensor de Difusão , Esclerose Múltipla/diagnóstico por imagem , Adulto , Análise de Variância , Atrofia/diagnóstico por imagem , Atrofia/etiologia , Mapeamento Encefálico , Progressão da Doença , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Ventrículos Laterais/diagnóstico por imagem , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Fatores Sexuais
10.
Ophthalmol Glaucoma ; 1(1): 15-22, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-32672627

RESUMO

PURPOSE: To develop and test the performance of a deep learning-based algorithm for glaucomatous disc identification using monoscopic fundus photographs. DESIGN: Fundus photograph database study. PARTICIPANTS: Four thousand three hundred ninety-four fundus photographs, including 3768 images from previous Sydney-based clinical studies and 626 images from publicly available online RIM-ONE and High-Resolution Fundus (HRF) databases with definitive diagnoses. METHODS: We merged all databases except the HRF database, and then partitioned the dataset into a training set (80% of all cases) and a testing set (20% of all cases). We used the HRF images as an additional testing set. We compared the performance of the artificial intelligence (AI) system against a panel of practicing ophthalmologists including glaucoma subspecialists from Australia, New Zealand, Canada, and the United Kingdom. MAIN OUTCOME MEASURES: The sensitivity and specificity of the AI system in detecting glaucomatous optic discs. RESULTS: By using monoscopic fundus photographs, the AI system demonstrated a high accuracy rate in glaucomatous disc identification (92.7%; 95% confidence interval [CI], 91.2%-94.2%), achieving 89.3% sensitivity (95% CI, 86.8%-91.7%) and 97.1% specificity (95% CI, 96.1%-98.1%), with an area under the receiver operating characteristic curve of 0.97 (95% CI, 0.96-0.98). Using the independent online HRF database (30 images), the AI system again accomplished high accuracy, with 86.7% in both sensitivity and specificity (for ophthalmologists, 75.6% sensitivity and 77.8% specificity) and an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.76-1.00). CONCLUSIONS: This study demonstrated that a deep learning-based algorithm can identify glaucomatous discs at high accuracy level using monoscopic fundus images. Given that it is far easier to obtain monoscopic disc images than high-quality stereoscopic images, this study highlights the algorithm's potential application in large population-based disease screening or telemedicine programs.

11.
Front Hum Neurosci ; 12: 473, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30618673

RESUMO

Previous studies have revealed brain adaptations to injury that occurs in optic neuritis (ON); however, the mechanisms underlying the functional connectivity (FC) and gray matter volume (GMV) changes in ON have not been clarified. Here, 51 single attack ON patients and 45 recurrent attacks ON patients were examined using structural MRI and resting-state functional MRI (RS-fMRI), and compared to 49 age- and gender-matched healthy controls (HC). FC analysis with a seed in primary visual cortex (V1 area) was used to assess the differences among three groups. Whole brain GMV was assessed using voxel-based morphometry (VBM). Correlation analyses were performed between FC results, structural MRI and clinical variables. We found positive correlations between the Paced Auditory Serial Addition Test (PASAT) score and FC in V1 area with bilateral middle frontal gyrus. Disease duration is significantly negatively related to FC in V1 area with the left inferior parietal lobule. Compared to the HC, single attack ON patients were found to have decreased FC values in the frontal, temporal lobes, right inferior occipital gyrus, right insula, right inferior parietal lobule, and significant increased FC values in the left thalamus. Recurrent attacks ON patients had the same pattern with single attack ON. No significant differences were found in brain GMV among three groups. This study provides the imaging evidence that impairment and compensation of V1 area connectivity coexist in ON patients, and provides important insights into the underlying neural mechanisms of ON.

12.
J Colloid Interface Sci ; 512: 767-774, 2018 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-29112927

RESUMO

Resistive switching behaviour can be classified into digital and analog switching based on its abrupt and gradual resistance change characteristics. Realizing the transition from digital to analog switching in the same device is essential for understanding and controlling the performance of the devices with various switching mechanisms. Here, we investigate the resistive switching in a device made with strontium titanate (SrTiO3) nanoparticles using X-ray diffractometry, scanning electron microscopy, Raman spectroscopy, and direct electrical measurements. It is found that the well-known rupture/formation of Ag filaments is responsible for the digital switching in the device with Ag as the top electrode. To modulate the switching performance, we insert a reduced graphene oxide layer between SrTiO3 and the bottom FTO electrode owing to its good barrier property for the diffusion of Ag ions and high out-of-plane resistance. In this case, resistive switching is changed from digital to analog as determined by the modulation of interfacial resistance under applied voltage. Based on that controllable resistance, potentiation and depression behaviours are implemented as well. This study opens up new ways for the design of multifunctional devices which are promising for memory and neuromorphic computing applications.

13.
Proc Natl Acad Sci U S A ; 113(45): 12709-12714, 2016 Nov 08.
Artigo em Inglês | MEDLINE | ID: mdl-27791192

RESUMO

The structure of the intact monomeric ATP synthase from the fungus, Pichia angusta, has been solved by electron cryo-microscopy. The structure provides insights into the mechanical coupling of the transmembrane proton motive force across mitochondrial membranes in the synthesis of ATP. This mechanism requires a strong and integral stator, consisting of the catalytic α3ß3-domain, peripheral stalk, and, in the membrane domain, subunit a and associated supernumerary subunits, kept in contact with the rotor turning at speeds up to 350 Hz. The stator's integrity is ensured by robust attachment of both the oligomycin sensitivity conferral protein (OSCP) to the catalytic domain and the membrane domain of subunit b to subunit a. The ATP8 subunit provides an additional brace between the peripheral stalk and subunit a. At the junction between the OSCP and the apparently stiff, elongated α-helical b-subunit and associated d- and h-subunits, an elbow or joint allows the stator to bend to accommodate lateral movements during the activity of the catalytic domain. The stator may also apply lateral force to help keep the static a-subunit and rotating c10-ring together. The interface between the c10-ring and the a-subunit contains the transmembrane pathway for protons, and their passage across the membrane generates the turning of the rotor. The pathway has two half-channels containing conserved polar residues provided by a bundle of four α-helices inclined at ∼30° to the plane of the membrane, similar to those described in other species. The structure provides more insights into the workings of this amazing machine.

14.
Neurocomputing ; 177: 75-88, 2016 Feb 12.
Artigo em Inglês | MEDLINE | ID: mdl-27688597

RESUMO

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

15.
Neuroinformatics ; 14(4): 387-401, 2016 10.
Artigo em Inglês | MEDLINE | ID: mdl-27184384

RESUMO

The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative back-tracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises. By evaluating the proposed pipeline with the data provided by the Diadem challenge and the recent BigNeuron project, Rivulet is shown to be robust to challenging microscopic imagestacks. We discussed the algorithm design in technical details regarding the relationships between the proposed algorithm and the other state-of-the-art neuron tracing algorithms.


Assuntos
Encéfalo/citologia , Imageamento Tridimensional/métodos , Técnicas de Rastreamento Neuroanatômico/métodos , Neurônios/citologia , Software , Algoritmos , Animais , Humanos , Microscopia Confocal , Reconhecimento Automatizado de Padrão
16.
Front Aging Neurosci ; 8: 23, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26941639

RESUMO

The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed nine types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI, and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.

17.
IEEE Trans Biomed Eng ; 63(5): 1058-1069, 2016 05.
Artigo em Inglês | MEDLINE | ID: mdl-26372117

RESUMO

Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Semântica , Algoritmos , Neoplasias Encefálicas/diagnóstico , Bases de Dados Factuais , Humanos , Neoplasias Pulmonares/diagnóstico
18.
Biochem J ; 468(1): 167-75, 2015 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-25759169

RESUMO

The ATP synthases have been isolated by affinity chromatography from the mitochondria of the fungal species Yarrowia lipolytica, Pichia pastoris, Pichia angusta and Saccharomyces cerevisiae. The subunit compositions of the purified enzyme complexes depended on the detergent used to solubilize and purify the complex, and the presence or absence of exogenous phospholipids. All four enzymes purified in the presence of n-dodecyl-ß-D-maltoside had a complete complement of core subunits involved directly in the synthesis of ATP, but they were deficient to different extents in their supernumerary membrane subunits. In contrast, the enzymes from P. angusta and S. cerevisiae purified in the presence of n-decyl-ß-maltose neopentyl glycol and the phospholipids 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine, cardiolipin (diphosphatidylglycerol) and 1-palmitoyl-2-oleoyl-sn-glycero-3-[phospho-rac-(1-glycerol)] had a complete complement of core subunits and also contained all of the known supernumerary membrane subunits, e, f, g, j, k and ATP8 (or Aap1), plus an additional new membrane component named subunit l, related in sequence to subunit k. The catalytic domain of the enzyme from P. angusta was more resistant to thermal denaturation than the enzyme from S. cerevisiae, but less stable than the catalytic domain of the bovine enzyme, but the stator and the integrity of the transmembrane proton pathway were most stable in the enzyme from P. angusta. The P. angusta enzyme provides a suitable source of enzyme for studying the structure of the membrane domain and properties associated with that sector of the enzyme complex.


Assuntos
Proteínas Fúngicas/química , Proteínas Fúngicas/isolamento & purificação , Fungos/enzimologia , ATPases Mitocondriais Próton-Translocadoras/química , ATPases Mitocondriais Próton-Translocadoras/isolamento & purificação , Sequência de Aminoácidos , Animais , Bovinos , Cromatografia de Afinidade , Estabilidade Enzimática , Proteínas Fúngicas/genética , Fungos/genética , ATPases Mitocondriais Próton-Translocadoras/genética , Dados de Sequência Molecular , Pichia/enzimologia , Pichia/genética , Estrutura Terciária de Proteína , Subunidades Proteicas , Saccharomyces cerevisiae/enzimologia , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/química , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/isolamento & purificação , Homologia de Sequência de Aminoácidos , Especificidade da Espécie , Yarrowia/enzimologia , Yarrowia/genética
19.
Brain Inform ; 2(3): 167-180, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-27747507

RESUMO

Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.

20.
Brain Inform ; 2(3): 181-195, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-27747508

RESUMO

The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

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