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A series of 8 cases of elastofibroma is reported, and the clinical, pathological and imaging features and different therapeutic modalities are reviewed. On this basis, we suggest an algorithm for the diagnosis and treatment of elastofibroma. Briefly, marginal excision is the treatment of choice in symptomatic patients, while followup appears to be a good solution in asymptomatic ones.

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Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yielding good results, even deeper architectures and alternative methods for feature fusion at different resolutions have been scarcely investigated for medical applications. In this work we propose to implement segmentation via an encoderdecoder architecture which differs from any other previously published method since (i) it employs a very deep architecture based on residual learning and (ii) combines features via a convolutional Long Short Term Memory (LSTM), instead of concatenation or summation. The intuition is that the memory mechanism implemented by LSTMs can better integrate features from different scales through a coarse-to-fine strategy; hence the name Coarse-to-Fine Context Memory (CFCM). We demonstrate the remarkable advantages of this approach on two datasets: the Montgomery county lung segmentation dataset, and the EndoVis 2015 challenge dataset for surgical instrument segmentation. * Maximilian Baust is now working for