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.     

Efficacy and Feasibility of Allogeneic Hematopoietic Stem-Cell Transplantation in the Treatment of Refractory Acute Myeloid Leukemia

Background

Refractory acute myeloid leukemia (AML) includes AML includes failure of disease to respond to standard induction chemotherapy, relapse within 6 months after first CR, and 2 or more relapses. The outcome of these patients is usually very poor; only a small proportion can be rescued by allogenic hematopoietic stem-cell transplantation (allo-HSCT). The aim of this study was to evaluate the efficacy and feasibility of allo-HSCT in patients with refractory AML.

Alternative impression technique for a speech-aid prosthesis.

OBJECTIVE: Prosthetic treatment for speech disorders attributable to surgically acquired soft palate defects are introduced. CASE: A patient who underwent soft palate resection for cancer is presented. The resected portion of the soft palate was confined to the posterior segments. A prosthesis with a speech bulb was adapted to the patient. CONCLUSION: Excellent restoration of speech and improvement of velopharyngeal function was achieved following placement of the special prosthesis.     

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.     

Metastasis of Prostate Adenocarcinoma to Testis

Metastatic carcinoma to the testis is very rare. Metastasis of prostate adenocarcinoma to testis was detected incidentally after bilateral orchiectomy for hormonal management of metastatic prostate carcinoma. The metastatic lesion was not identified in physical examination or in macroscopic dissection of the testis after surgery. Microscopy revealed an adenocarcinoma which, given the history of the patient and a positive immunohistochemical stain for PSA, was identified as metastatic prostatic adenocarcinoma.     

Brucella peritonitis and leucocytoclastic vasculitis due to Brucella melitensis.

Brucellosis is a multisystemic disease that rarely leads to a fatal outcome. While reticuloendothelial system organs are mostly affected, peritonitis and posthepatitic cirrhosis are also complications of brucellosis, though they are very rare. Brucella spp. can also trigger immunological reactions. We report a case of brucellosis with peritonitis, renal failure and leucocytoclastic vasculitis caused by Brucella melitensis, which led to a fatal outcome. Brucellosis should be considered in the differential diagnosis of vasculitic diseases, especially in endemic areas.     

Clinical and immunological effects of azithromycin in Behçet''s disease

Background: The aim of this study was to evaluate the effects of azithromycin on mucocutaneous manifestations, oral health and immune response in Behçet's disease (BD). Methods: Eight BD patients with active mucocutaneous symptoms were treated with azithromycin for 4 weeks. Oral health, clinical manifestations and in vitro interleukin (IL)‐12, interferon (IFN)‐γ, IL‐10 and monocyte chemotactic protein (MCP)‐1 responses were evaluated before and after treatment. Results: The number of folliculitic lesions, healing time of oral ulcers and scores of plaque indexes (PLIs) were lower after azithromycin treatment (P < 0.05). Scores of PLIs correlated positively with the healing time of oral ulcers (P = 0.02). Although a trend towards increased stimulated IL‐10 responses with azithromycin was observed, no statistically significant difference was found. Stimulated and unstimulated MCP‐1, IFN‐γ and IL‐12 responses were similar before and after treatment (P > 0.05). Conclusion: Azithromycin was observed to be effective in decreasing folliculitic lesions and fastening the healing time of oral ulcers in BD.     

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.     

Adiponectin is an independent determinant of insulin resistance in women with polycystic ovary syndrome.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is frequently associated with insulin resistance and a consequent increased risk of metabolic diseases. The aim of the present study was to investigate the role of adiponectin in insulin resistance in PCOS women. MATERIALS AND METHODS: Forty-seven patients with PCOS and 23 control subjects, matched for age and body mass index (BMI), were enrolled in the study. Clinical, metabolic and hormonal parameters and adiponectin levels were measured, and HOMA-IR score (homeostasis model assessment-insulin resistance index) was calculated for each subject. RESULTS: There was no difference in adiponectin levels between PCOS patients and the control group. However, adiponectin levels were negatively correlated with obesity-associated parameters and HOMA-IR score in PCOS patients and controls. As adiponectin is modulated by BMI we adjusted for BMI among the PCOS patients, and found a negative correlation between adiponectin levels and HOMA-IR score (r = -0.51, p < 0.001). Adiponectin and BMI were independent determinants of insulin resistance in PCOS patients (adjusted R2 = 0.66, p < 0.001). CONCLUSION: Adiponectin did not seem to be actively involved in the pathogenesis of PCOS. However, adiponectin levels were independently associated with insulin resistance in PCOS patients, suggesting that adiponectin might play a role in the complicated metabolic abnormalities of PCOS.