Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 5 de 5
Mais filtros

Base de dados
Intervalo de ano de publicação
Artigo em Inglês | MEDLINE | ID: mdl-33907990


PURPOSE: Microelectrode recordings (MERs) are a significant clinical indicator for sweet spots identification of implanted electrodes during deep brain stimulation of the subthalamic nucleus (STN) surgery. As 1D MERs signals have the unboundedness, large-range, large-amount and time-dependent characteristics, the purpose of this study is to propose an automatic and precise identification method of sweet spots from MERs, reducing the time-consuming and labor-intensive human annotations. METHODS: We propose an automatic identification method of sweet spots from MERs for electrodes implantation in STN-DBS. To better imitate the surgeons' observation and obtain more intuitive contextual information, we first employ the 2D Gramian angular summation field (GASF) images generated from MERs data to perform the sweet spots determination for electrodes implantation. Then, we introduce the convolutional block attention module into convolutional neural network (CNN) to identify the 2D GASF images of sweet spots for electrodes implantation. RESULTS: Experimental results illustrate that the identification result of our method is consistent with the result of doctor's decision, while our method can achieve the accuracy and precision of 96.72% and 98.97%, respectively, which outperforms state-of-the-art for intraoperative sweet spots determination. CONCLUSIONS: The proposed method is the first time to automatically and accurately identify sweet spots from MERs for electrodes implantation by the combination an advanced time series-to-image encoding way with CBAM-enhanced networks model. Our method can assist neurosurgeons in automatically detecting the most likely locations of sweet spots for electrodes implantation, which can provide an important indicator for target selection while it reduces the localization error of the target during STN-DBS surgery.

IEEE Trans Med Imaging ; 40(5): 1363-1376, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33507867


To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge ( provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

Med Image Anal ; 67: 101832, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33166776


Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.

Comput Biol Med ; 109: 290-302, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31100582


BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNets) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI. This paper presents a novel recurrent interleaved attention network (RIANet) to comprehensively tackle this issue. METHOD: The proposed RIANet can efficiently reuse parameters to encode richer representative features via introducing a recurrent feedback structure, Clique Block, which incorporates both forward and backward connections between different layers with the same resolution. Further, we integrate a plug-and-play interleaved attention (IA) block to modulate the information passed to the decoding stage of RIANet by effectively fusing multi-level contextual information. In addition, we improve the discrimination capability of our RIANet through a deep supervision mechanism with weighted losses. RESULTS: The performance of RIANet has been extensively validated in the segmentation contest of the ACDC 2017 challenge held in conjunction with MICCAI 2017, with mean Dice scores of 0.942 (left ventricular), 0.923 (right ventricular) and 0.910 (myocardium) for cardiac MRI segmentation. Besides, we visualize intermediate features of our RIANet using guided backpropagation, which can intuitively depict the effects of our proposed components in feature representation. CONCLUSION: Experimental results demonstrate that our RIANet have achieved competitive segmentation results with fewer parameters compared with the state-of-the-art approaches, corroborating the effectiveness and robustness of our proposed RIANet.

Coração/diagnóstico por imagem , Imageamento Tridimensional , Imagem por Ressonância Magnética , Redes Neurais de Computação , Humanos
Pediatr Dermatol ; 33(4): 424-8, 2016 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-27292264


OBJECTIVES: To investigate the development and clinical characteristics of nail changes in hand, foot, and mouth disease (HFMD). METHODS: A telephone survey was conducted with the parents of patients diagnosed with HFMD in the Fourth General Hospital of Nanhai from June to August 2013 to document nail changes within 3 months of diagnosis of HFMD. RESULTS: Valid survey results were obtained from 273 cases. Definitive nail changes were identified in 56 patients (20.5%). More boys (25.8%) than girls (10.6%) (p < 0.01) showed changes. The age distribution ranged from 1 to 5 years, and nail changes were rare in children younger than 1 year of age (p < 0.01). Nail changes were usually seen 1 to 2 months after the onset of HFMD and lasted for 1 to 8 weeks, most for approximately 4 weeks. Toenails or fingernails could be affected and the changes were more likely to occur synchronously. Fingernails were more commonly involved than toenails. When both fingernails and toenails were involved, this typically occurred synchronously. Although there were cases with all toenails and fingernails involved (16.1%), we did not encounter any instances involving 13 to 19 nails. The nail changes mainly presented as onychomadesis. Spontaneous recovery without special treatment was the course for all patients. No relapse or new nail involvement was identified. CONCLUSIONS: Nail change associated with HFMD usually occurs within 1 to 2 months after onset, mainly presents as onychomadesis, and is a self-limited process. Possible mechanisms are discussed.

Doença de Mão, Pé e Boca/complicações , Doenças da Unha/etiologia , Distribuição por Idade , Criança , Pré-Escolar , China/epidemiologia , Feminino , Inquéritos Epidemiológicos , Humanos , Lactente , Masculino , Unhas , Distribuição por Sexo