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.     

广西防城港市2017年活禽市场环境样本禽流感监测

目的 了解防城港市活禽市场环境禽流感病毒监测情况,为科学评判疫情并采取适当的防控措施提供理论依据。方法 2017年3—4月在全市活禽市场外环境采集笼具擦物、冼禽污水、禽粪、砧板擦物、禽类饮用水等标本,用荧光定量PCR法检测禽流感病毒FluA、H5、H9、H7核酸,并进行统计学分析。结果 采集全市36个活禽市场外环境标本1 855份,阳性393份,阳性率为21.19%,其中H5阳性28份,H7阳性2份,H9阳性198份,H5和H9混合阳性37份,其他亚型（FluA阳性,H5、H7、H9 阴性）128份;不同地区标本阳性率差异有统计学意义（P<0.05）;城区和乡镇市场采集的标本的阳性率差异无统计学意义。5种类型的标本中,洗禽污水的阳性率最高,其次是砧板擦物, 再次是禽类饮用水和笼具擦物,禽粪的阳性率最低,差异有统计学意义（P<0.05）。结论 防城港市活禽市场环境普遍存在高致病性禽流感病毒的污染,H9亚型是主要的病原体,采取了综合防控措施后,防城港市没有发生人间禽流感疫情。     

青岛市2018年1例疟疾死亡病例分析

目的 分析青岛市2018年1例输入性恶性疟的死亡原因,为预防疟疾死亡病例发生提供参考。方法 访谈接诊医生及患者家属,对患者病历和流行病学调查资料进行分析。结果 患者刘某,男,35岁,青岛市城阳区人,2018年3月19日到科特迪瓦务工,7月17日回国。25日患者到韩国办事,26日下午出现发冷、发热、出汗、头痛、浑身酸痛等症状,26—27日自服退烧药无好转,28日上午到韩国某医院就诊,未明确诊断,下午出现腹泻、呕吐症状,29日下午回国,14∶59到城阳区人民医院就诊,精神萎靡,经询问患者有非洲出国史可能患疟疾,16∶30将患者转诊至疟疾治疗定点医院,17∶50到达青岛市传染病医院时患者呈深昏迷状态,医生考虑患者病情危重且该院正在改建无ICU病房,建议转至有抢救条件的综合医院抢救。19:40患者转入海慈医疗集团ICU病房,21∶00被确诊为恶性疟,因病情危重,患者于30日14∶15死于多脏器功能衰竭。结论 对于输入性疟疾,根据流行病学史、临床症状和实验室检测结果尽早明确诊断,尽早使用抗疟药治疗,是避免出现死亡病例的关键。     

血清胱抑素C和尿微量白蛋白联合检测在人类免疫缺陷病毒感染者肾功能损伤检测中的应用价值

目的评估血肌酐和尿常规的常规检测基础上联合血清胱抑素C和尿微量白蛋白检测在人类免疫缺陷病毒（HIV）感染者肾功能损伤检测中的应用价值。 方法以2019年2～5月于首都医科大学附属北京地坛医院感染一科住院的169例HIV感染者为研究对象，完善其尿常规、尿微量白蛋白、血肌酐、血清胱抑素C检测；分析尿蛋白及尿微量白蛋白的阳性检出率及其差异，血肌酐升高及血清胱抑素C升高的比例及其差异。计算应用替诺福韦酯（TDF）及合并丙型肝炎、高血压、糖尿病、肿瘤的肾功能损伤的相对危险度。 结果169例HIV感染者中尿常规示尿蛋白阳性者5例（3.0%），尿微量白蛋白升高者17例（10.1%），两者阳性检出率差异具有统计学意义（χ² = 5.9、P = 0.007）。血肌酐升高者10例（5.9%），血清胱抑素C升高者20例（11.8%），两者阳性检出率差异具有统计学意义（χ² = 3.0、P = 0.042）。在尿常规和血肌酐检测的基础上联合检测尿微量白蛋白和血清胱抑素C的总体阳性检出率为49例（30.0%）。CD4⁺ T淋巴细胞计数< 200个/μl与≥ 200个/μl组患者血清胱抑素C水平分别为0.94（0.83，1.05）mg/L、0.85（0.77，0.95）mg/L，差异具有统计学意义（Z =-3.02、P = 0.003）。应用TDF及合并丙型肝炎、高血压、糖尿病、肿瘤的肾功能损伤的相对危险度分别为1.1、1.5、1.9、2.2和1.4。 结论HIV感染者中，单纯以尿常规为依据评估有无蛋白尿，以血肌酐水平为依据评估肾小球滤过功能会低估肾功能损伤的患病率。在常规检测血肌酐和尿常规的基础上联合检测血清胱抑素C和尿微量白蛋白在提高HIV感染者肾功能损伤检出率方面具有重要的应用价值。低CD4⁺ T淋巴细胞计数、应用TDF及合并丙型肝炎、高血压、糖尿病、肿瘤均为肾功能损伤的危险因素。     

In memoriam Professor Em. Jan G. van den Tweel (1942–2020)

Wiener Medizinische Wochenschrift -     

普萘洛尔联合其他治疗措施治疗婴幼儿血管瘤有效率的网状Meta分析

目的:采用网状Meta分析方法,将普萘洛尔联合其他治疗与各对照治疗措施进行对比。方法:以“普萘洛尔”,“血管瘤”为关键词检索CNKI、VIP、万方数据;以“propranolol”“hemangioma”为关键词检索Cochrane Library、Embase、pubmed。检索期限为建库至2019年6月1日。采用STATA14.0软件Network程序包进行数据分析。结果:共纳入18篇文献,涉及8项治疗措施,共纳入1469例血管瘤患者。网状Meta分析结果显示:4种联合治疗方式与单纯口服普萘洛尔相比疗效均优于单纯口服普萘洛尔治疗。普萘洛尔联合注射平阳霉素、普萘洛尔联合外用噻吗洛尔、普萘洛尔联合敷贴器的有效率均优于对应的注射平阳霉素、外用噻吗洛尔及敷贴器。各治疗措施的有效性排序为:普萘洛尔联合敷贴器>普萘洛尔联合外用噻吗洛尔>普萘洛尔联合口服糖皮质激素>普萘洛尔联合注射平阳霉素>敷贴器照射>外用噻吗洛尔>口服普萘洛尔>注射平阳霉素。结论:对于婴幼儿血管瘤的治疗,普萘洛尔联合治疗措施疗效均优于单纯的口服普萘洛尔治疗,其中普萘洛尔联合敷贴器的疗效最佳。