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.     

Skin closure with 2-octyl cyanoacrylate and polyester mesh after primary total knee arthroplasty offers superior cosmetic outcomes and patient satisfaction compared to staples: a prospective trial

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...     

应用国产封堵器在治疗先天性心脏病的临床疗效分析

目的 总结国产封堵器经皮和经胸途径治疗先天性心脏病的临床疗效。方法 回顾性分析我院2013年1月至2017年12月在X线透视下或单纯超声心动图引导下采用经皮穿刺股静脉或股动静脉法，或者食管超声心动图监测下经胸小切口行先天性心脏病封堵1186例，其中经皮X线下封堵1081例、经皮单纯超声引导下封堵42例、经胸封堵63例；其中动脉导管未闭（ patent ductus arteriole，PDA）426例、房间隔缺损（atrial septal defect，ASD）363例、室间隔缺损（ventricular septal defect，VSD）348例、卵圆孔未闭11例、房间隔缺损合并室间隔缺损9例、房间隔缺损合并动脉导管未闭6例、房间隔缺损合并肺动脉瓣狭窄（pulmonary stenosis，PS）12例、动脉导管未闭合并肺动脉瓣狭窄8例、主肺动脉侧支封堵3例[经胸封堵66例改为63例，PDA443例改为426例]。结果 全组病例成功率98.2％（1165/1186），无死亡病例。随访1~36个月，术后第1、3、6、12个月及术后每年常规行超声心动图及心电图检查。术后第1、6、12个月的随访率分别为 92.9%（1102/1186）、84.1%（998/1186）、70.5%（836/1186）。超声心动图提示少量残余分流（<3 mm）18例；三尖瓣少量反流33例，中量反流5例；主动脉瓣轻度反流5例，中度反流1例；心律失常Ⅲ°房室传导阻滞（Avionics Bulletin，AVB[房室传导阻滞（Avionics Bulletin，AVB）]）1 例，Ⅱ°AVB 3例，完全性左束支3例，交界性心动过速3例，交界性逸搏2例。结论 国产封堵器在先天性心脏病封堵治疗中具有成功率高、创伤小、并发症低、操作容易、疗效确切、恢复快等特点，是治疗先天性心脏病的理想方法。     

The RECIST criteria compared to conventional response evaluation after peptide receptor radionuclide therapy in patients with neuroendocrine neoplasms

Annals of Nuclear Medicine - The Response Evaluation Criteria In Solid Tumors (RECIST) is the most used radiological method for evaluating response after peptide receptor radionuclide therapy...     

Uncalcified Synovial Chondromatosis in the Pisotriquetral Joint

Synovial chondromatosis is a rare lesion in the wrist, but some cases in the distal radioulnar joint have been reported and previous case reports emphasize joint calcifications, shown on preoperative plain radiographs. We report an extremely uncommon case of synovial chondromatosis in the pisotriquetral joint, in which radiographs and magnetic resonance imaging did not demonstrate apparent calcified bodies. In our case, for the accurate diagnosis and treatment, surgical exploration of the joint and synovectomy with removal of loose bodies was performed.