A computer-implemented method for automatically registering intraoperative ultrasound imaging of an object with a pre-operative co-modality imaging of the object, wherein the ultrasound imaging is generated by propagating ultrasound waves along a plurality of scanlines through the object, the computer-implemented method comprising: segmenting the ultrasound imaging with a convolutional neural network "CNN" to detect a surface of the object; calibrating the ultrasound imaging to reflect a variation in propagation speed of the ultrasound waves through the object based on an estimated propagation speed, wherein estimating the propagation speed includes: comparing first and second steered frames (66a, 66b) of the ultrasound imaging with a third frame of the ultrasound imaging that is perpendicular to the first and second steered frames (66a, 66b by computing a first absolute difference between pixel intensities of the first steered frame and the third frame, and computing a second absolute difference between pixel intensities of the second steered frame and the third frame, wherein the pixel intensities are compounded with a propagation speed; computing a sum of the first and second absolute differences; optimizing the sum by varying the compounded propagation speed to minimize a cost function, the cost function being defined as the sum of the first and second absolute differences per absolute set of all pixels within the object region in the ultrasound imaging; and estimating the propagation speed of the ultrasound waves through the object based on the optimized sum; temporally calibrating the ultrasound imaging with respect to a tracking coordinate system by: creating a point cloud of the surface based on a tracked position of an ultrasound probe; calculating a set of projection values of the point cloud to a 3D vector that is oriented parallel to an average direction of the plurality of scanlines; and calculating a temporal lag for the ultrasound imaging based on the set of projection values comprising choosing a value which minimizes variance of the set of values and provides a cost function at a global minimum at the optimization of the temporal lag; and automatically registering the segmented and calibrated ultrasound imaging to the co-modality imaging. segmenting the ultrasound imaging with a convolutional neural network "CNN" to detect a surface of the object; calibrating the ultrasound imaging to reflect a variation in propagation speed of the ultrasound waves through the object based on an estimated propagation speed, wherein estimating the propagation speed includes: comparing first and second steered frames (66a, 66b) of the ultrasound imaging with a third frame of the ultrasound imaging that is perpendicular to the first and second steered frames (66a, 66b by computing a first absolute difference between pixel intensities of the first steered frame and the third frame, and computing a second absolute difference between pixel intensities of the second steered frame and the third frame, wherein the pixel intensities are compounded with a propagation speed; computing a sum of the first and second absolute differences; optimizing the sum by varying the compounded propagation speed to minimize a cost function, the cost function being defined as the sum of the first and second absolute differences per absolute set of all pixels within the object region in the ultrasound imaging; and estimating the propagation speed of the ultrasound waves through the object based on the optimized sum; comparing first and second steered frames (66a, 66b) of the ultrasound imaging with a third frame of the ultrasound imaging that is perpendicular to the first and second steered frames (66a, 66b by computing a first absolute difference between pixel intensities of the first steered frame and the third frame, and computing a second absolute difference between pixel intensities of the second steered frame and the third frame, wherein the pixel intensities are compounded with a propagation speed; computing a sum of the first and second absolute differences; optimizing the sum by varying the compounded propagation speed to minimize a cost function, the cost function being defined as the sum of the first and second absolute differences per absolute set of all pixels within the object region in the ultrasound imaging; and estimating the propagation speed of the ultrasound waves through the object based on the optimized sum; temporally calibrating the ultrasound imaging with respect to a tracking coordinate system by: creating a point cloud of the surface based on a tracked position of an ultrasound probe; calculating a set of projection values of the point cloud to a 3D vector that is oriented parallel to an average direction of the plurality of scanlines; and calculating a temporal lag for the ultrasound imaging based on the set of projection values comprising choosing a value which minimizes variance of the set of values and provides a cost function at a global minimum at the optimization of the temporal lag; and creating a point cloud of the surface based on a tracked position of an ultrasound probe; calculating a set of projection values of the point cloud to a 3D vector that is oriented parallel to an average direction of the plurality of scanlines; and calculating a temporal lag for the ultrasound imaging based on the set of projection values comprising choosing a value which minimizes variance of the set of values and provides a cost function at a global minimum at the optimization of the temporal lag; and automatically registering the segmented and calibrated ultrasound imaging to the co-modality imaging.