A computer- implemented method for spinal anatomy segmentation, comprising: combining a fully convolutional network with a residual neural network; training the combined fully convolutional network with the residual neural network from end to end; receiving at least one medical image of a spinal anatomy; and applying the fully convolutional network with the residual neural network to the received at least one medical image to segment at least one vertebral body from the at least one medical image of the spinal anatomy; characterized in that: the method further includes: extracting more than one three-dimensional vertebral surfaces from the segmented at least one medical image, wherein the extracted more than one three-dimensional vertebral surfaces comprises a first surface and a second surface, wherein the first surface corresponds to an inferior endplate of a top vertebra, wherein the second surface corresponds to a superior endplate of a lower vertebra, and wherein the lower vertebra is adjacent to the top vertebra; and based on the extracted more than one three-dimensional vertebral surfaces, modeling an intervertebral disc mesh; and wherein the fully convolutional network is designed to make the intensity distribution of all input images similar to each other. combining a fully convolutional network with a residual neural network; training the combined fully convolutional network with the residual neural network from end to end; receiving at least one medical image of a spinal anatomy; and applying the fully convolutional network with the residual neural network to the received at least one medical image to segment at least one vertebral body from the at least one medical image of the spinal anatomy; characterized in that: the method further includes: extracting more than one three-dimensional vertebral surfaces from the segmented at least one medical image, wherein the extracted more than one three-dimensional vertebral surfaces comprises a first surface and a second surface, wherein the first surface corresponds to an inferior endplate of a top vertebra, wherein the second surface corresponds to a superior endplate of a lower vertebra, and wherein the lower vertebra is adjacent to the top vertebra; and based on the extracted more than one three-dimensional vertebral surfaces, modeling an intervertebral disc mesh; and wherein the fully convolutional network is designed to make the intensity distribution of all input images similar to each other. the method further includes: extracting more than one three-dimensional vertebral surfaces from the segmented at least one medical image, wherein the extracted more than one three-dimensional vertebral surfaces comprises a first surface and a second surface, wherein the first surface corresponds to an inferior endplate of a top vertebra, wherein the second surface corresponds to a superior endplate of a lower vertebra, and wherein the lower vertebra is adjacent to the top vertebra; and based on the extracted more than one three-dimensional vertebral surfaces, extracting more than one three-dimensional vertebral surfaces from the segmented at least one medical image, wherein the extracted more than one three-dimensional vertebral surfaces comprises a first surface and a second surface, wherein the first surface corresponds to an inferior endplate of a top vertebra, wherein the second surface corresponds to a superior endplate of a lower vertebra, and wherein the lower vertebra is adjacent to the top vertebra; and based on the extracted more than one three-dimensional vertebral surfaces, modeling an intervertebral disc mesh; and wherein the fully convolutional network is designed to make the intensity distribution of all input images similar to each other. <EOS>