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.

With respect to recent advances in remote sensing technologies, the spatial resolution of airborne and spaceborne sensors is getting finer, which enables us to precisely analyze even small objects on the Earth. This fact has made the research area of developing efficient approaches to extract spatial and contextual information highly active. Among the existing approaches, morphological profile and attribute profile (AP) have gained great attention due to their ability to classify remote sensing data. This paper proposes a novel approach that makes it possible to precisely extract spatial and contextual information from remote sensing images. The proposed approach is based on extinction filters, which are used here for the first time in the remote sensing community. Then, the approach is carried out on two well-known high-resolution panchromatic data sets captured over Rome, Italy, and Reykjavik, Iceland. In order to prove the capabilities of the proposed approach, the obtained results are compared with the results from one of the strongest approaches in the literature, i.e., APs, using different points of view such as classification accuracies, simplification rate, and complexity analysis. Results indicate that the proposed approach can significantly outperform its alternative in terms of classification accuracies. In addition, based on our implementation, profiles can be generated in a very short processing time. It should be noted that the proposed approach is fully automatic. Index Terms-Attribute profile (AP), extinction profile (EP), image classification, random forest (RF), remote sensing data. I. INTRODUCTION U NDOUBTEDLY, supervised classification plays a fundamental role in the analysis of remote sensing images, and many applications, such as crop monitoring, forest applications, urban development, mapping and tracking, and risk management, can be handled by an efficient classifier [1].

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Systemic lupus erythematosus is a chronic, inflammatory, immune-mediated disease affecting 0.1% of the general population. Neuropsychiatric manifestations in systemic lupus erythematosus have been more frequently recognized and reported in recent years, occurring in up to 75% of patients during the disease course. Magnetic resonance imaging is known to be a useful tool for the detection of structural brain abnormalities in neuropsychiatric systemic lupus erythematosus patients because of the excellent soft-tissue contrast observed with MRI and the ability to acquire multiplanar images. In addition to conventional magnetic resonance imaging techniques to evaluate the presence of atrophy and white matter lesions, several different magnetic resonance imaging techniques have been used to identify microstructural or functional abnormalities. This review will highlight different magnetic resonance imaging techniques, including the advanced magnetic resonance imaging methods used to determine central nervous system involvement in systemic lupus erythematosus.