A method for determining anatomy from raw medical acquisition data, the method comprising: obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, using the raw medical acquisition data as input and said one or more values for parameters of the parameterized model as output, by generating associations between model parameters and raw data from the acquisition; acquiring raw medical acquisition data for a selected patient from transmission and collection of energy and particles traveling through and originating from the selected patient's body; and using the trained machine learning system on the raw medical acquisition data acquired for said patient to determine a parameter value for a patient-specific parameterized model of the patient; wherein said raw acquisition data is constituted by data selected in the group consisting of: a sinogram of a CT system or a time series acquisition thereof, echo measurements or Doppler measurements of receivers of an ultrasound system or a time series acquisition thereof, electric field measurements from an electric field imaging system or a time series acquisition thereof, a k-space acquisition corresponding to an magnetic resonance signal or a time series acquisition thereof, a sinogram associated with x-ray beam attenuation or a time series acquisition thereof, and magnetic relaxation measurement(s) by one or more coils of a parallel MRI system or a time series acquisition thereof. obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, using the raw medical acquisition data as input and said one or more values for parameters of the parameterized model as output, by generating associations between model parameters and raw data from the acquisition; acquiring raw medical acquisition data for a selected patient from transmission and collection of energy and particles traveling through and originating from the selected patient's body; and using the trained machine learning system on the raw medical acquisition data acquired for said patient to determine a parameter value for a patient-specific parameterized model of the patient; wherein said raw acquisition data is constituted by data selected in the group consisting of: a sinogram of a CT system or a time series acquisition thereof, echo measurements or Doppler measurements of receivers of an ultrasound system or a time series acquisition thereof, electric field measurements from an electric field imaging system or a time series acquisition thereof, a k-space acquisition corresponding to an magnetic resonance signal or a time series acquisition thereof, a sinogram associated with x-ray beam attenuation or a time series acquisition thereof, and magnetic relaxation measurement(s) by one or more coils of a parallel MRI system or a time series acquisition thereof.