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The pathology in Duchenne muscular dystrophy (DMD) is characterized by degenerating muscle fibers, inflammation, fibro-fatty infiltrate, and edema, and these pathological processes replace normal healthy muscle tissue. Themdxmouse model is one of the most commonly used preclinical models to study DMD. Mounting evidence has emerged illustrating that muscle disease progression varies considerably inmdxmice, with inter-animal differences as well as intra-muscular differences in pathology in individualmdxmice. This variation is important to consider when conducting assessments of drug efficacy and in longitudinal studies. Magnetic resonance imaging (MRI) is a non-invasive method that can be used qualitatively or quantitatively to measure muscle disease progression in the clinic and in preclinical models. Although MR imaging is highly sensitive, image acquisition and analysis can be time intensive. The purpose of this study was to develop a semi-automated muscle segmentation and quantitation pipeline that can quickly and accurately estimate muscle disease severity in mice. Herein, we show that the newly developed segmentation tool accurately divides muscle. We show that measures of skew and interdecile range based on segmentation sufficiently estimate muscle disease severity in healthy wildtype and diseasedmdxmice. Moreover, the semi-automated pipeline reduced analysis time by nearly 10-fold. Use of this rapid, non-invasive, semi-automated MR imaging and analysis pipeline has the potential to transform preclinical studies, allowing for pre-screening of dystrophic mice prior to study enrollment to ensure more uniform muscle disease pathology across treatment groups, improving study outcomes.