Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness.Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners.The challenge remains open for future submissions and provides a public platform for method evaluation.

HighlightsThis work presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.This work introduces the related information to the challenge, discusses the results from the conventional methods and deep learning-based algorithms, and provides insights to the future research.The challenge provides a fair and intuitive comparison framework for methods developed and being developed for WHS.The challenge provides the training datasets with manually delineated ground truths and evaluation for an ongoing development of MM-WHS algorithms.

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their

Objective Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generation from MR images. Deep learning-based algorithms have recently gained momentum for a number of medical image analysis applications. In this work, a novel sCT generation algorithm based on deep learning adversarial semantic structure (DL-AdvSS) is proposed for MRI-guided attenuation correction in brain PET/MRI. Materials and methods The proposed DL-AdvSS algorithm exploits the ASS learning framework to constrain the synthetic CT generation process to comply with the extracted structural features from CT images. The proposed technique was evaluated through comparison to an atlas-based sCT generation method (Atlas), previously developed for MRI-only or PET/MRI-guided radiation planning. Moreover, the commercial segmentation-based approach (Segm) implemented on the Philips TF PET/MRI system was included in the evaluation. Clinical brain studies of 40 patients who underwent PET/CT and MR imaging were used for the evaluation of the proposed method under a twofold cross validation scheme. Results The accuracy of cortical bone extraction and CT value estimation were investigated for the three different methods. Atlas and DL-AdvSS exhibited similar cortical bone extraction accuracy resulting in a Dice coefficient of 0.78 ± 0.07 and 0.77 ± 0.07, respectively. Likewise, DL-AdvSS and Atlas techniques performed similarly in terms of CT value estimation in the cortical bone region where a mean error (ME) of less than −11 HU was obtained. The Segm approach led to a ME of −1025 HU. Furthermore, the quantitative analysis of corresponding PET images using the three approaches assuming the CT-based attenuation corrected PET (PET CTAC) as reference demonstrated comparative performance of DL-AdvSS and Atlas techniques with a mean standardized uptake value (SUV) bias less than 4% in 63 brain regions. In addition, less that 2% SUV bias was observed in the cortical bone when using Atlas and DL-AdvSS approaches. However, Segm resulted in 14.7 ± 8.9% SUV underestimation in the cortical bone. Conclusion The proposed DL-AdvSS approach demonstrated competitive performance with respect to the state-of-the-art atlasbased technique achieving clinically tolerable errors, thus outperforming the commercial segmentation approach used in the clinic.

Congenital and progressive hearing impairment is a common distressing disease. The progressive dominant hearing loss DFNA28 in human is associated with a frameshift mutation of Grainyhead-like 2 (GRHL2) but its etiology and mechanism remain unknown. Here we report a zebrafish grhl2b(T086) mutant line in which grhl2b expression is interrupted by an insertion of a Tol2 transposon element. The mutants exhibit enlarged otocysts, smaller or eliminated otoliths, malformed semicircular canals, insensitiveness to sound stimulation and imbalanced swimming motion. Since grainyhead-like family members can regulate epithelial adhesion, we examined the expression of some genes encoding junction proteins in mutants. We show that the expression of claudin b (cldnb) and epcam is abolished or dramatically reduced and apical junctional complexes are abnormal in otic epithelial cells of mutant embryos. Co-injection of cldnb and epcam mRNA could largely rescue the mutant phenotype. Injection of human wild-type GRHL2 mRNA but not the mutant GRHL2 mRNA derived from DFNA28 patients into grhl2b(T086) mutant embryos could rescue the inner-ear defects. Furthermore, we demonstrate that Grhl2b directly binds to the enhancers and promotes the expression of cldnb and epcam. Thus, this work reveals an evolutionarily conserved function of Grhl2 in otic development and provides a fish model for further studying mechanisms of Grhl2-related hearing loss.