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Intraparenchymal hemorrhage (IPH) after a traumatic injury has been associated with poor neurological outcomes. Although IPH may result from the initial mechanical trauma, the blood and its breakdown products have potentially deleterious effects. Further, the degree of IPH has been correlated with injury severity and the extent of subsequent recovery. Therefore, accurate evaluation and quantification of IPH following traumatic spinal cord injury (SCI) is important to define treatments' effects on IPH progression and secondary neuronal injury. Imaging modalities, such as magnetic resonance imaging (MRI) and ultrasound (US), have been explored by researchers for the detection and quantification of IPH following SCI. Both quantitative and semiquantitative MRI and US measurements have been applied to objectively assess IPH following SCI, but the optimal methods for doing so are not well established. Studies in animal SCI models (rodent and porcine) have explored US and histological techniques in evaluating SCI and have demonstrated the potential to detect and quantify IPH. Newer techniques using machine learning algorithms (such as convolutional neural networks [CNN]) have also been studied to calculate IPH volume and have yielded promising results. Despite long-standing recognition of the potential pathological significance of IPH within the spinal cord, quantifying IPH with MRI or US is a relatively new area of research. Further studies are warranted to investigate their potential use. Here, we review the different and emerging quantitative MRI, US, and histological approaches used to detect and quantify IPH following SCI.