Clinical studies have shown that skeletal muscle mass, sarcopenia and muscle atrophy can be used as predictive indicators for morbidity and mortality after various surgical procedures and in different medical treatment methods. At the same time, the major psoas muscle has been has been used as a tool to assess total muscle volume. From the image processing side it has the advantage of being one of the few muscles that are not surrounded by other muscles at all times, thereby allowing simpler segmentation than in other muscles. The muscle is fully visible on abdominal CT scans, which are for example performed in clinical workups before surgery. Therefore, automatic analysis of the psoas major muscle in routine CT scans would aid in the assessment of sarcopenia without the need for additional scans or examinations. To this end, we present a method for fully automated segmentation of the psoas major muscle in abdominal CT scans using a combination of methods for semantic segmentation and shape analysis. Our method outperforms available approaches for this task, additionally we show a good correlation between muscle volume and population parameters in different clinical datasets.
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- Fully Automated Segmentation of the Psoas Major Muscle in Clinical CT Scans
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