Abstract: The invention relates to a computer-implemented method of liver resection surgery planning, comprising the steps of: processing (E2) preoperative computed tomographic images (10) of a patient to generate liver, liver lesion (ss) and venous vessels segmentations; pruning the venous vessels segmentation to retain only major vessels and determining (E3) a primary hepatic zone (PHZ) from the retained major vessels; determining (E4) at least one quantitative feature from the primary hepatic zone, the liver segmentation and the liver lesion(s) segmentation; processing (E5) the determined quantitative features with a classification model to determine a liver resection complexity score for the patient.

Abstract: The present invention relates to method for medical imaging, the method being characterized in that it comprises the implementation, by a data processor (11b) of a second server (1b), of steps of: (a) Obtaining at least one candidate pre-contrast image and a candidate contrast image respectively depicting a body part prior to and after an injection of a first dose of contrast agent, wherein said first dose is higher than a predetermined low dose, the predetermined reduced dose being lower than a predetermined standard dose; a convolutional neural network, CNN, being trained to reconstruct, from at least a pre-contrast input image and a low dose contrast input image respectively depicting a body part prior to and after an injection of said low dose of contrast agent, a standard dose contrast image depicting said body part after an injection of said standard dose of contrast agent; (b) Simulating an injection of a second dose of contrast agent which is higher than the standard dose, wherein said simulating comp

Abstract: The present invention relates to a method for processing an inputted perfusion sequence, by means of a convolutional neural network, CNN, the method being characterized in that it comprises the implementation, by a data processor (11b) of a second server (1b), of steps of: (b) extracting, using an encoder branch of the CNN, a plurality of initial n+1-dimensional features maps representative of the inputted perfusion sequence at different scales, n?3, said CNN further comprising a decoder branch and skip connections between the encoder branch and the decoder branch, each skip connection projecting the initial n+1-dimensional features maps into initial n-dimensional feature maps; (c) generating, using said decoder branch of the CNN, a plurality of enriched n-dimensional feature maps also representative of representative of the inputted perfusion sequence at different scales, each enriched n-dimensional feature map incorporating the information from the initial n-dimensional feature maps of smaller or equal scal