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.     

The effect of metoclopramide on gastric emptying in traumatic brain injury.

OBJECTIVE: Gastric paresis in traumatic brain injury (TBI) hinders the effectiveness of enteral support in this patient group. In this study we have investigated the effect of metoclopramide on gastric emptying in TBI patients. METHOD: In this prospective, randomized, controlled, double-blind study, 19 TBI patients with Glasgow Coma Scale scores of 3-11 were included. In all patients, enteral nutrition was commenced with a nasogastric feeding tube within 48 hours of trauma. Patients were randomized into two groups. In the metoclopramide (M) group, 10 mg metoclopramide was delivered intravenously three times daily for 5 days. In the control (C) group, an equal volume of saline was administered. Besides demographics, gastric emptying according to a paracetamol absorption test at days 0 and 5, time to reach target nutritional requirements, gastric residues, intolerance to feeding, nutritional complications, and clinical outcomes were recorded for each patient. RESULTS: The gastric residue rates were 2.7+/-7.4 mL and 8.1+/-17.7 mL per 100 patient days for groups C and M respectively (p=0.408). Similarly, feeding intolerance and complication rates did not significantly differ between groups C and M, (respectively p=0.543 and 0.930). Gastric emptying parameters also were similar between the study groups. CONCLUSION: We were unable to document any advantage to using metoclopramide in TBI patients. Simple intragastric enteral feeding with close monitoring of the possible complications seems to be sufficient with acceptable morbidity rates.     

Optimization of self-reference thermometry using complex field estimation.

Referenceless, or self-reference, thermometry is a technique for mapping temperature differences in the region of interest (ROI) using the baseline phase estimated by extrapolating the field in the surrounding region for estimation (RFE) and subtracting the estimated baseline from the measured field. In the present work a self-reference technique based on complex field estimation using 2D polynomials comprising complex-valued coefficients was proposed and optimized. Numerical simulations with a Gaussian-profiled phase distribution demonstrated that the ROI radius had to be 2.3-2.5 times the standard deviation (SD) of the Gaussian function in order to keep the error below 8% of the peak phase change. The area ratio between the ROI and the RFE had to be larger than 2.0 to maintain the error level. Based on the simulations, and phantom and volunteer experiments, the complex-based method with independently optimized polynomial orders for the two spatial dimensions was compared with the phase-based method using the similar-order optimization strategy. The complex-based method appeared to be useful when phase unwrapping was not removed. Otherwise, the phase-based method yielded equivalent results with less polynomial orders.     

Intracoronary shunt reduces postoperative troponin leaks: a prospective randomized study.

OBJECTIVE: The purpose of this study was to evaluate whether intracoronary shunt usage reduced the myocardial damage on the basis of the cardiac markers when compared with the shuntless anastomosis in off-pump coronary artery bypass grafting (OPCABG) surgery of isolated left anterior descending artery lesions. METHODS: Forty patients who had stable angina with isolated left anterior descending (LAD) coronary artery lesion undergoing OPCABG surgery were randomized into two groups. Shunt group consisted of 20 patients who had OPCABG using intracoronary shunt, whereas the shuntless group consisted of 20 patients who underwent OPCABG without using intracoronary shunt. Cardiac troponin I, CK, and CK-MB before and 24h after the surgery were assessed in the groups. RESULTS: There were no deaths in the study. The two groups were similar with respect to sex and age. Duration of LIMA-LAD anastomosis was significantly higher in the shunt group (p=0.01). There was no significant difference between the groups concerning the preoperative and postoperative CK and CK-MB levels. The preoperative troponin I levels of the groups were not different (p=0.238; NS), whereas postoperative levels of this marker was significantly higher in the shuntless group (p=0.003). CONCLUSION: Intracoronary shunt reduced the postoperative troponin I levels significantly, so it may be indicated in the patients who are thought to be susceptible to transient ischemia.     

Fast decomposition of water and lipid using a GRASE technique with the IDEAL algorithm.

Three-point Dixon techniques achieve good lipid-water separation by estimating the phase due to field inhomogeneities. Recently it was demonstrated that the combination of an iterative algorithm (iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL)) with a fast spin-echo (FSE) three-point Dixon method yielded robust lipid-water decomposition. As an alternative to FSE, the gradient- and spin-echo (GRASE) technique has been developed for efficient data collection. In this work we present a method for lipid-water separation by combining IDEAL with the GRASE technique. An approach to correct for errors in the lipid-water decomposition caused by phase distortions due to the switching of the readout gradient polarities inherent to GRASE is presented. The IDEAL-GRASE technique is demonstrated in phantoms and in vivo for various applications, including pelvic, musculoskeletal, and (breath-hold) cardiac imaging.     

Ausfallhonorar wegen Nichterscheinens zu einem Exklusiv-Termin

Abstrakt 1. Nimmt ein Patient einen ihm von seinem (Zahn-)Arzt einger?umten Exklusiv-Termin nicht wahr, obwohl er auf dessen Eigenschaft ausdrücklich hingewiesen wurde, so hat er dem (Zahn-)Arzt den Behandlungsausfall abzüglich eines angemessenen Eigenanteils des (Zahn-)Arztes zu ersetzen. 2. Die Ersatzpflicht tritt auch dann ein, wenn der Patient den Termin nicht in der in dem Behandlungsvertrag vorgesehenen Frist absagt. Eine hierfür seitens des (Zahn-)Arztes bestimmte Frist von zwei Tagen vor Behandlungsbeginn stellt sich für den Patienten grunds?tzlich auch nicht als unangemessene Benachteiligung i.S. des § 307 BGB dar. 3. Ein Anspruch des Arztes entf?llt auch bei nur mündlicher Vereinbarung nicht unter dem Gesichtspunkt des § 4 Abs. 5b BMV-Z, denn diese Vorschrift ist teleologisch dahin zu reduzieren, dass nur zahn?rztliche Honoraransprüche aus erfolgten Behandlungen schriftlich vereinbart werden müssen. Soweit es jedoch um einen vertraglichen Anspruch wegen einer Leistungsst?rung geht, vermag das Schriftformerfordernis des § 4 Abs. 5b BMV-Z grunds?tzlich nicht einzugreifen. (Leits?tze des Bearbeiters)     

Role of IL-2 in rat fetal thymocyte development

Early during rat thymus ontogeny, an important proportion of thymocytes expresses IL-2R and contains IL-2 mRNA. To investigate the role of the IL-2-IL-2R complex in rat T cell maturation, we supplied either recombinant rat IL-2 or blocking anti-CD25 mAb to rat fetal thymus organ cultures (FTOC) under several experimental conditions. The IL-2 treatment initially stimulated the growth of thymocytes and, as a result, induced T cell differentiation, but the continuous addition of IL-2 to rat FTOC, as well as the anti-CD25 administration, resulted in cell number decrease and inhibition of thymocyte maturation. These results indicate that immature rat thymocytes bear functional high- affinity IL-2R and that IL-2 promotes T cell differentiation as a consequence of its capacity to stimulate cell proliferation. Modifications in TCR alpha beta repertoire and increased numbers of NKR- P1+ cells, largely NK cells, were also observed in IL-2-treated FTOC. Furthermore, IL-2-responsiveness of different thymocyte subsets changed throughout thymic ontogeny. Immature CD4-CD8-cells responded to IL-2 in two stages, early in thymus development and around birth, in correlation with the maturation of two distinct waves of thymic cell progenitors. Mature CD8+ thymocytes maximally responded to IL-2 around birth, supporting a role for IL-2 in the increased proliferation of mature thymocytes observed in vivo in the perinatal period. Taken together, these findings support a role for IL-2 in rat T cell development.