A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

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.     

Maîtrise des risques liés à la production des fluides de dialyse dans une unité de télédialyse

Objective

The “Centre Hospitalier Francois Dunan” is located on an isolated island and ensures patients care in hemodialysis thanks to telemedicine support. Many research studies have demonstrated the importance of hemodialysis fluids composition to reduce morbidity in patients on chronic hemodialysis. The aim of this study was to identify the risks inherent in the production of dialysis fluids in a particular context, in order to set up an improvement action plan to improve risk control on the production of dialysis fluids.

Chest wall pain following lung stereotactic body radiation therapy using 48 Gy in three fractions: A search for predictors

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.

解剖锁定钢板联合同种异体腓骨治疗肱骨近端骨折疗效的Meta分析

文题释义：肱骨近端骨折：肱骨近端包括肱骨头及大结节、小结节，中老年人骨质疏松及低能量损伤可导致肱骨近端骨折。 同种异体腓骨：取自于人体异体，经过加工处理，去除其免疫原性，保留其骨性结构，可用于移植修复骨缺损，起到支撑作用。 背景：肱骨近端骨折是临床常见骨折，但对肱骨近端内侧柱缺乏支撑的骨折目前仍是治疗难点，并发症常见，失败率较高。 目的：比较解剖锁定钢板联合同种异体腓骨与单纯解剖锁定钢板治疗肱骨近端骨折的疗效。 方法：使用计算机检索PubMed、Embase、Cochrane Library、Google Scholar、中国知网、万方、维普数据库，检索时间均从建库到2020年2月。检索国内外关于对比研究解剖锁定钢板联合同种异体腓骨与单纯解剖锁定钢板治疗肱骨近端骨折疗效的文献。2名研究员根据纳入和排除标准分别独立筛选文献，提取数据，评估文献中的偏倚风险。纳入12篇相关文献使用RevMan 5.2软件将以下指标进行Meta分析，包括影像学数据、功能评分和并发症。结果与结论：①通过文献检索、根据纳入和排除标准，12篇文献纳入研究，其中11篇为回顾性队列研究，1篇为随机对照研究；纳入研究文献质量高，但GRADE证据质量级别较低。②共纳入958例患者，其中解剖锁定钢板联合同种异体腓骨组411例，单纯解剖锁定钢板组547例；③Meta分析结果显示，解剖锁定钢板联合同种异体腓骨组术后1年肱骨头高度差值(MD=-2.40，95%CI：-2.49至-2.31)、颈干角差值(MD= -6.14，95%CI：-6.62至-5.67)、目测类比评分(MD=-0.22，95%CI：-0.35至-0.08)、肩关节功能评分(MD=4.12，95%CI：2.18-6.06)，上肢伤残评分(MD=-10.32，95%CI：-13.44至-7.19)、术后2年的目测类比评分(MD=-0.37，95%CI：-0.55至-0.19)、肩关节功能评分(MD=5.07，95%CI：2.86-7.27)、总体并发症(OR=0.31，95%CI：0.20-0.48)及肱骨头螺钉切出(OR=0.25，95%CI：0.11-0.55)均明显优于单纯解剖锁定钢板组(P < 0.05)，肱骨头坏死(OR=0.94，95%CI：0.47-1.88)，两组间差异无显著性意义(P > 0.05)；④因此，较弱的证据提示，肱骨近端解剖锁定钢板联合同种异体腓骨治疗肱骨近端骨折的短期疗效优于解剖锁定钢板，可减少并发症的发生，促进功能恢复。ORCID: 0000-0002-8486-3932(阳运康) 中国组织工程研究杂志出版内容重点：人工关节；骨植入物；脊柱；骨折；内固定；数字化骨科；组织工程