A computer based method for finding and characterizing an anatomic mass of a cyst and providing a class indicating the presence of a serum in the cyst and a subclass indicating the presence of one or more serous loculi in the cyst and/or the number thereof, the method comprising the steps of: (a) obtaining at least an ultrasound image of an examined anatomic entity including the anatomic mass; (b) using a first algorithm based on a neural network elaborating the image for outputting the characterization as a cyst and/or the class of the cyst; (c) using a second algorithm of computational graphics for: i. identifying inner regions of the cyst, each of said inner regions being a fluid region or a boundary region, based on an at least a first and a second threshold applied to a mathematical function of the echogenicity of the image configured such as to increase the differentiation between a low echogenic level fluid region and a high echogenic level surrounding boundary solid region; ii. classifying the inner region as a serous region or a solid region based on a quantitative morphologic parameter of the inner region and on a aggregation model generating an association between at least a fluid internal region and a boundary internal region when a proximity condition is verified between the regions; iii. counting the number of serous regions as serous loculi and/or a number of solid regions; (d) providing an image classification comprising the class and the sub-class based on the combination of outputs from the first and second algorithm. (a) obtaining at least an ultrasound image of an examined anatomic entity including the anatomic mass; (b) using a first algorithm based on a neural network elaborating the image for outputting the characterization as a cyst and/or the class of the cyst; (c) using a second algorithm of computational graphics for: i. identifying inner regions of the cyst, each of said inner regions being a fluid region or a boundary region, based on an at least a first and a second threshold applied to a mathematical function of the echogenicity of the image configured such as to increase the differentiation between a low echogenic level fluid region and a high echogenic level surrounding boundary solid region; ii. classifying the inner region as a serous region or a solid region based on a quantitative morphologic parameter of the inner region and on a aggregation model generating an association between at least a fluid internal region and a boundary internal region when a proximity condition is verified between the regions; iii. counting the number of serous regions as serous loculi and/or a number of solid regions; i. identifying inner regions of the cyst, each of said inner regions being a fluid region or a boundary region, based on an at least a first and a second threshold applied to a mathematical function of the echogenicity of the image configured such as to increase the differentiation between a low echogenic level fluid region and a high echogenic level surrounding boundary solid region; ii. classifying the inner region as a serous region or a solid region based on a quantitative morphologic parameter of the inner region and on a aggregation model generating an association between at least a fluid internal region and a boundary internal region when a proximity condition is verified between the regions; iii. counting the number of serous regions as serous loculi and/or a number of solid regions; (d) providing an image classification comprising the class and the sub-class based on the combination of outputs from the first and second algorithm. <EOS>