-Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases.In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.Keywords: Brain extraction, skull stripping, patch-based segmentation, multi-resolution, MRI, BET ** Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. IntroductionBrain extraction (or skull stripping) is an important step in many neuroimaging analyses, such as registration, tissue classification, and segmentation. While methods such as the estimation of intensity normalization fields and registration do not require perfect brain masks, other methods such as measuring cortical thickness rely on very accurate brain extraction to work properly. For instance, failure to remove the dura may lead to an overestimation of cortical thickness (van der Kouwe et al., 2008), while removing part of the brain would lead to an underestimation. In cases of incorrect brain extraction, subjects may be excluded from further processing, a potentially expensive consequence for many studies. The solution of manually correcting the brain masks is a labour intensive and time-consuming task that is highly sensitive to inter-and intra-rater variability (Warfield et al., 2004).An accurate brain extraction method should exclude all tissues external to the brain, such as skull, dura, and eyes, without removing any part of the brain. The number of methods proposed to address the brain segmentation problem reflects the importance of accurate and robust brain extraction. During th...

We thank Marlies Hankel and Jake Carroll for enabling the access to the necessary compute capabilities, Joshua Arnold for initial discussions and implementations that laid the foundations of this project and Matthew Cronin for valuable feedback on the bioRxiv preprint.

Key Messages• The aim of this study was to describe the inter-individual and intra-individual variability in segmental volume of the undisturbed colon between two observations and the effect of defecation on the segmental volumes.• Variability of segmental colorectal volumes was assessed with a novel semi-automatic MRI-based technique in healthy males in a controlled fasting state.• Despite being a highly dynamic organ, the colon displays low intra-individual variability and the technique is sensitive to the changes in segmental colorectal volume that occur from defecation. AbstractBackground Segmental distribution of colorectal volume is relevant in a number of diseases, but clinical and experimental use demands robust reliability and validity. Using a novel semi-automatic magnetic resonance imaging-based technique, the aims of this study were to describe: (i) inter-individual and intra-individual variability of segmental colorectal volumes between two observations in healthy subjects and (ii) the change in segmental colorectal volume distribution before and after defecation. Methods The inter-individual and intra-individual variability of four colorectal volumes (cecum/ ascending colon, transverse, descending, and rectosigmoid colon) between two observations (separated by 52 AE 10) days was assessed in 25 healthy males and the effect of defecation on segmental colorectal volumes was studied in another seven healthy males. Key Results No significant differences between the two observations were detected for any segments (All p > 0.05). Inter-individual variability varied across segments from low correlation in cecum/ascending colon (intra-class correlation coefficient [ICC] = 0.44) to moderate correlation in the descending colon (ICC = 0.61) and high correlation in the transverse (ICC = 0.78), rectosigmoid (ICC = 0.82), and total volume (ICC = 0.85). Overall intra-individual variability was low (coefficient of variance = 9%). After defecation the volume of the rectosigmoid decreased by 44% (p = 0.003). The change in rectosigmoid volume was associated with the true fecal volume (p = 0.02). Conclusions & Inferences Imaging of segmental colorectal volume, morphology, and fecal accumulation is advantageous to conventional methods in its low variability, high spatial resolution, and its absence of contrast-enhancing agents and irradiation. Hence, the method is suitable for future clinical and interventional studies and for characterization of defecation physiology.

Purpose: To automatically assess the aggressiveness of prostate cancer (PCa) lesions using zonal-specific image features extracted from diffusion weighted imaging (DWI) and T2W MRI. Methods: Region of interest was extracted from DWI (peripheral zone) and T2W MRI (transitional zone and anterior fibromuscular stroma) around the center of 112 PCa lesions from 99 patients. Image histogram and texture features, 38 in total, were used together with a k-nearest neighbor classifier to classify lesions into their respective prognostic Grade Group (GG) (proposed by the International Society of Urological Pathology 2014 consensus conference). A semi-exhaustive feature search was performed (1-6 features in each feature set) and validated using threefold stratified cross validation in a one-versus-rest classification setup. Results: Classifying PCa lesions into GGs resulted in AUC of 0.87, 0.88, 0.96, 0.98, and 0.91 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5 for the peripheral zone, respectively. The results for transitional zone and anterior fibromuscular stroma were AUC of 0.85, 0.89, 0.83, 0.94, and 0.86 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5, respectively. Conclusion: This study showed promising results with reasonable AUC values for classification of all GG indicating that zonal-specific imaging features from DWI and T2W MRI can be used to differentiate between PCa lesions of various aggressiveness.

Regional colon volumes were comparable to previous findings using fully manual segmentation. The method showed good agreement between observers and may be used in future studies of gastrointestinal disorders to assess colon and fecal volume and colon morphology. Novel insight into morphology and quantitative assessment of the colon using this method may provide new biomarkers for constipation and abdominal pain compared to radiography which suffers from poor reliability.