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.     

深圳市全人口结核病控制实施效果评价

目的　研究深圳市实施包含非户籍人口在内的全人口结核病控制模式。方法 在全人口中推行以发现和彻底治疗传染性肺结核病为主的现代结核病控制和监测策略。结果 从1993年至2001年,登记涂阳肺结核8483例,治愈率96.3%。肺结核疫情明显下降,深圳市户籍人口涂阳患病率下降55.9%,非户籍人口涂阳患病率下降38.6%。结论 实施全人口结核病控制是必要的、可行的和科学的。     

异搏定防治流行性出血热初期急性肾功能衰竭的疗效观察

在综合疗法的基础上加用异搏定3～5天治疗流行性出血热(EHF)发热后期病人41例,在尿蛋白转阴、越期率,特别是越少尿期平明显优于对照组,但对 BUN 水平的影响与对照组无异,异搏定对防治 EHF 急性肾衰具有一定疗效。     

套式PCR检测细菌16SrRNA基因

目的　应用套式PCR建立快速检查方法来检测血液中是否含有细菌。方法　采用细菌 1 6SrRNA基因通用引物 ,通过套式PCR方法对细菌进行扩增 ,并对其灵敏度、特异性作一评价。结果　临床常见细菌扩增反应为阳性 ,其他病原微生物及人类基因组DNA为阴性。结论　本法具有敏感、快速、特异、准确的优点 ,是一种可靠的实验手段     

高脂血症时血清二乙基对硝基苯磷酸酯酶活性的变化

目的 :研究tritonWR 13 3 9致小鼠高脂血症时血清二乙基对硝基苯磷酸酯酶 (ParaoxonaseI ,PON 1)活性的变化及PON 1活性与血浆脂质含量之间的关系。方法 :在小鼠尾静脉注射tritonWR13 3 9后的 3h ,6h ,12h ,2 4h ,48h ,72h ,眼球摘除术取血 ,用酶标法测定小鼠血浆总胆固醇 (TC) ,甘油三酯 (TG ) ,高密度脂蛋白 -胆固醇 (HDL -C) ,载脂蛋白AI (ApoAI) ,载脂蛋白B (ApoB含量 ) ,并计算出ApoAI/ApoB ,HDL -C/TC比值 ,用分光光度计法测定PONI活性。结果 :TritonWR 13 3 9尾静脉注射后 3h ,小鼠血浆TC和TG含量即急剧增高 ,与对照组相比 ,P <0 .0 1,分别在注射后 2 4h和 3h达到峰值 ,随后逐渐下降 ,其中血浆TG含量在注射后 48h即恢复正常水平。tritonWR13 3 9尾静脉注射后 3h ,小鼠血浆HDL -C和ApoB含量急剧增高 ,与对照组相比 ,P <0 .0 5,分别在注射后 2 4h和 6h达到峰值 ,随后逐渐下降 ,分别在注射后 48h和 72h恢复正常水平。tritonWR13 3 9尾静脉注射后 3h ,小鼠血浆ApoAI含量即急剧降低 ,与对照组相比 ,P <0 .0 5,随后逐渐增高 ,在注射后 6h即恢复正常水平。tritonWR 13 3 9尾静脉注射后 3h ,小鼠血浆ApoAI/ApoB和HDL-C/TC比值减少 ,与对照组相比 ,P <0 .0 1,随后逐渐增高 ,其中ApoAI/ApoB比值在 72h恢复     

染色内镜和放大内镜技术是提高早期大肠癌诊治水平的重要手段

应用常规内镜技术难以发现大肠平坦型病变和凹陷型病变。近年来染色内镜和放大内镜技术已经发展成熟,在国外已获广泛应用,可以发现大肠微小病变和早期大肠癌。应用腺管开口分型方法可以预测肿瘤病变的组织学类型及肿瘤的浸润深度,据此可确定行内镜下粘膜剥离术或分片粘膜剥离术将肿瘤切除,抑或行外科手术治疗。在当前我国的胃肠内镜医疗界,应广泛开展染色内镜和放大内镜的临床应用,以早期发现大肠病变,提高我国大肠癌的内镜诊治水平。     

葡聚糖硫酸钠致溃疡性结肠炎小鼠模型的建立

目的　建立小鼠溃疡性结肠炎模型。方法　给小鼠自由饮用5％葡聚糖硫酸钠（DSS）溶液7d后,改为蒸馏水自由饮用10d,如此进行4个循环,每天观察症状。在第1个循环和第4个循环后剖杀小鼠,取出整段结肠行实体显微镜观察及切片组织学研究。结果　所观察的症状、实体显微镜像、组织学病理改变均与人类溃疡性结肠炎类似。结论　此模型制作方法简便,重复性良好,可应用于多种实验研究。