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Musculoskeletal diseases such as sarcopenia and osteoporosis are majorobstacles to health during aging. Although dual-energy X-ray absorptiometry(DXA) and computed tomography (CT) can be used to evaluate musculoskeletalconditions, frequent monitoring is difficult due to the cost and accessibility(as well as high radiation exposure in the case of CT). We propose a method(named MSKdeX) to estimate fine-grained muscle properties from a plain X-rayimage, a low-cost, low-radiation, and highly accessible imaging modality,through musculoskeletal decomposition leveraging fine-grained segmentation inCT. We train a multi-channel quantitative image translation model to decomposean X-ray image into projections of CT of individual muscles to infer the leanmuscle mass and muscle volume. We propose the object-wise intensity-sum loss, asimple yet surprisingly effective metric invariant to muscle deformation andprojection direction, utilizing information in CT and X-ray images collectedfrom the same patient. While our method is basically an unpaired image-to-imagetranslation, we also exploit the nature of the bone's rigidity, which providesthe paired data through 2D-3D rigid registration, adding strong pixel-wisesupervision in unpaired training. Through the evaluation using a 539-patientdataset, we showed that the proposed method significantly outperformedconventional methods. The average Pearson correlation coefficient between thepredicted and CT-derived ground truth metrics was increased from 0.460 to0.863. We believe our method opened up a new musculoskeletal diagnosis methodand has the potential to be extended to broader applications in multi-channelquantitative image translation tasks. Our source code will be released soon.

MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume