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BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.1 Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.4,5 Although the extent of WMHs can be visually scored,6 the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.7 Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.8-11 More recently, a WMH segmentation challenge12 was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.12Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio13 and have shown promising results in MS lesion detection using 3D CNNs.14 Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.15 The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.16 The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.17 Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization18 framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.  相似文献   
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European Journal of Orthopaedic Surgery & Traumatology - The goals of this study were to compare patient satisfaction and wound-related complications in patients receiving 2-octyl cyanoacrylate...  相似文献   
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Purpose

Chest wall pain is an uncommon but bothersome late complication following lung stereotactic body radiation therapy. Despite numerous studies investigating predictors of chest wall pain, no clear consensus has been established for a chest wall constraint. The aim of our study was to investigate factors related to chest wall pain in a homogeneous group of patients treated at our institution.

Patients and methods

All 122 patients were treated with the same stereotactic body radiation therapy regimen of 48 Gy in three fractions, seen for at least 6 months of follow-up, and planned with heterogeneity correction. Chest wall pain was scored according to the Common Terminology Criteria for Adverse Events classification v3.0. Patient (age, sex, diabetes, osteoporosis), tumour (planning target volume, volume of the overlapping region between planning target volume and chest wall) and chest wall dosimetric parameters (volumes receiving at least 30, 40, and 50 Gy, the minimal doses received by the highest irradiated 1, 2, and 5 cm3, and maximum dose) were collected. The correlation between chest wall pain (grade 2 or higher) and the different parameters was evaluated using univariate and multivariate logistic regression.

Results

Median follow-up was 18 months (range: 6–56 months). Twelve patients out of 122 developed chest wall pain of any grade (seven with grade 1, three with grade 2 and two with grade 3 pain). In univariate analysis, only the volume receiving 30 Gy or more (P = 0.034) and the volume of the overlapping region between the planning target volume and chest wall (P = 0.038) significantly predicted chest wall pain, but these variables were later proved non-significant in multivariate regression.

Conclusion

Our analysis could not find any correlation between the studied parameters and chest wall pain. Considering our present study and the wide range of differing results from the literature, a reasonable conclusion is that a constraint for chest wall pain is yet to be defined.  相似文献   
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