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We present a semi-supervised segmentation framework for B-mode ultrasound imaging<\/strong>. It is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches<\/strong> to represent the ultrasound image and\u00a0user-assisted initialization with labels<\/strong>, which acts as soft priors. The segmentation problem is formulated\u00a0as a continuous minimum cut problem<\/strong> and solved with an efficient optimisation algorithm.<\/p>\n Works using fP-CMC should cite:<\/strong><\/p>\n Anca Ciurte, Xavier Bresson, Olivier Cuisenaire, Nawal Houhou, Sergiu Nedevschi, Jean-Philippe Thiran, Meritxell Bach Cuadra, Semi-Supervised Segmentation of Ultrasound Images based on Patch Representation and Continuous Min Cut<\/strong>. Plos One, Volume 9, Number 7, July 2014<\/em><\/a> <\/i>.\u00a0DOI: 10.1371\/journal.pone.0100972.<\/p>\n Ciurte, Anca, Cuisenaire, Olivier, Bresson, Xavier, & Bach Cuadra, Meritxell. (2020, March 24). Software fP-CMC: Fast Patch-\u00adbased Continuous Min-Cut segmentation. Semi-supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut.<\/strong> Zenodo. https:\/\/doi.org\/10.5281\/zenodo.3725998<\/p>\n