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The dearth of annotated data is a major hurdle in building reliable image segmentation models. Manual annotation of medical images is tedious, time-consuming, and significantly variable across imaging modalities. The need for annotation can be ameliorated by leveraging an annotation-rich source modality in learning a segmentation model for an annotation-poor target modality. In this paper, we introduce a diverse data augmentation generative adversarial network (DDA-GAN) to train a segmentation model for an unannotated target image domain by borrowing information from an annotated source image domain. This is achieved by generating diverse augmented data for the target domain by one-to-many source-to-target translation. The DDA-GAN uses unpaired images from the source and target domains and is an end-to-end convolutional neural network that (i) explicitly disentangles domain-invariant structural features related to segmentation from domain-specific appearance features, (ii) combines structural features from the source domain with appearance features randomly sampled from the target domain for data augmentation, and (iii) train the segmentation model with the augmented data in the target domain and the annotations from the source domain. The effectiveness of our method is demonstrated both qualitatively and quantitatively in comparison with the state of the art for segmentation of craniomaxillofacial bony structures via MRI and cardiac substructures via CT., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Idiopathic normal-pressure hydrocephalus (iNPH) is defined by ventriculomegaly, cognitive decline, urinary incontinence and gait problems. Vascular risk factors (VRF) are associated with iNPH but obstructive sleep apnea (OSA) -a well-known independent VRF- is seldom mentioned., Methods: We investigated the presence of sleep-disordered breathing in a prospective cohort of 31 consecutive unselected patients with iNPH using sleep questionnaires and nocturnal polysomnography (PSG)., Results: We found OSA in 90·3% (28/31) patients with iNPH; all had undiagnosed sleep abnormalities (snoring, awakenings, nocturia) and excessive daytime sleepiness (Epworth scale = 11·4 ± 6·4; normal <8). Nocturnal PSG showed moderate-to-severe OSA in 25 patients (80·6%) with mean apnea-hypopnea index (AHI) 31·6 ± 23·6/h; mean respiratory distress index (RDI) 34·5/h; and, mean SaO 2 desaturation at nadir, 82·2 ± 7·5%. The observed OSA prevalence is statistically significant: 90·3%, 95%CI 74·3-97·5; p = 0·000007. Other VRF included overweight body-mass index (BMI >25- < 30 kg/m 2 ) in 59%, hyperhomocysteinemia 57%, hypertension 43%, hyperlipidemia 39%, diabetes 32%, smoking 21%, coronary disease 18%, and previous stroke 10%., Conclusion: Abnormal sleep breathing is frequently associated with iNPH. Validation in larger series is required but we suggest including sleep evaluation in patients suspected of iNPH., (Copyright © 2018. Published by Elsevier B.V.)

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used to evaluate tumor permeability, yet measurements have not been directly validated in brain tumors. Our purpose was to compare estimates of forward leakage K(trans) derived from DCE-MRI to the estimates K obtained using [(14)C]aminoisobutyric acid quantitative autoradiography ([(14)C]AIB QAR), an established method of evaluating blood-tumor barrier permeability. Both DCE-MRI and [(14)C]AIB QAR were performed in five rats 9 to 11 days following tumor implantation. K(trans) in the tumor was estimated from DCE-MRI using the threeparameter general kinetic model and a measured vascular input function. K(i) was estimated from QAR data using regions of interest (ROI) closely corresponding to those used to estimate K(trans). K(trans) and K(i) correlated with each other for two independent sets of central tumor ROI (R = 0.905, P = .035; R = 0.933, P = .021). In an additional six rats, K(trans) was estimated on two occasions to show reproducibility (intraclass coefficient = 0.9993; coefficient of variance = 6.07%). In vivo blood-tumor permeability parameters derived from DCE-MRI are reproducible and correlate with the gold standard for quantifying blood tumor barrier permeability, [(14)C]AIB QAR.