A system for visualization and quantification of ultrasound imaging data, the system comprising: a display unit ; a processor communicatively coupled to the display unit and to an ultrasound imaging apparatus for generating an image from ultrasound data representative of a blood vessel and fluid flowing within the blood vessel, wherein the processor is configured to: generate vector field data including axial and lateral velocity components of the fluid flowing within the blood vessel; calculate velocity profiles across a diameter of the blood vessel for a plurality of locations along a wall of the blood vessel based on the axial and lateral velocity components; generate wall shear stress, WSS, visualization data based, at least in part, on the velocity profiles, the generating the WSS visualization data comprising computing WSS values for each of the plurality of locations along the wall; and cause the display unit to concurrently display the image including the blood vessel overlaid with the WSS visualization data, characterised in that the WSS visualization data comprises a color map of plaque-development risk estimates computed based, at least in part, on the WSS values, and based on a thresholding process, wherein the processor is communicatively coupled to a neural network , and wherein thresholds for the WSS values used in the thresholding process are provided by the neural network, the neural network being a network trained to output patient-specific thresholds for the WSS values based on an input comprising information about the patient. a display unit ; a processor communicatively coupled to the display unit and to an ultrasound imaging apparatus for generating an image from ultrasound data representative of a blood vessel and fluid flowing within the blood vessel, wherein the processor is configured to: generate vector field data including axial and lateral velocity components of the fluid flowing within the blood vessel; calculate velocity profiles across a diameter of the blood vessel for a plurality of locations along a wall of the blood vessel based on the axial and lateral velocity components; generate wall shear stress, WSS, visualization data based, at least in part, on the velocity profiles, the generating the WSS visualization data comprising computing WSS values for each of the plurality of locations along the wall; and cause the display unit to concurrently display the image including the blood vessel overlaid with the WSS visualization data, characterised in that the WSS visualization data comprises a color map of plaque-development risk estimates computed based, at least in part, on the WSS values, and based on a thresholding process, generate vector field data including axial and lateral velocity components of the fluid flowing within the blood vessel; calculate velocity profiles across a diameter of the blood vessel for a plurality of locations along a wall of the blood vessel based on the axial and lateral velocity components; generate wall shear stress, WSS, visualization data based, at least in part, on the velocity profiles, the generating the WSS visualization data comprising computing WSS values for each of the plurality of locations along the wall; and cause the display unit to concurrently display the image including the blood vessel overlaid with the WSS visualization data, characterised in that the WSS visualization data comprises a color map of plaque-development risk estimates computed based, at least in part, on the WSS values, and based on a thresholding process, wherein the processor is communicatively coupled to a neural network , and wherein thresholds for the WSS values used in the thresholding process are provided by the neural network, the neural network being a network trained to output patient-specific thresholds for the WSS values based on an input comprising information about the patient. <EOS>