This is a report on four persons in one family with a condition similar to that described by Ramon et al [Oral Surg 24:436-48, 1967] in two sibs born to a consanguineous couple. Our patients also had mental deficiency, epilepsy, cherubism due to fibrous dysplasia of the maxillae, gingival fibromatosis, hypertrichosis, and stunted growth. This appears to be an autosomal recessive trait in both families. Our patients are the second set reported with this syndrome; they also have juvenile rheumatoid arthritis, which was not described in the family reported by Ramon et al [Oral Surg 24:436-48, 1967]. We conclude that the Ramon syndrome should also include juvenile rheumatoid arthritis.

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Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to diagnose heart disease. However, the repetitive manual segmentation of these images requires considerable human effort and can decrease diagnostic accuracy. In recent years, several fully and semi-automatic approaches have been proposed, mainly using image-based, atlas, graphs, deformable models, and artificial intelligence methods. This paper presents a systematic mapping on the left ventricle segmentation, considering 74 studies published in the last decade. The main contributions of this review are: definition of the main segmentation challenges in these images; proposal of a new schematization, dividing the segmentation process into stages; categorization and analysis of the segmentation methods, including hybrid combinations; and analysis of the evaluation process, metrics, and databases. The performance of the methods in the most used public database is assessed, and the main limitations, weaknesses, and strengths of each method category are presented. Finally, trends, challenges, and research opportunities are discussed. The analysis indicates that methods from all categories can achieve good performance, and hybrid methods combining deep learning and deformable models obtain the best results. Methods still fail in specific slices, segment wrong regions, and produce anatomically impossible segmentations.

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