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bowel, vessels and visceral adipose tissue cyan). As shown in Figure 1, examinations were segmented manually into six classes, each with a unique color: (1) background pixels (external to the pelvis black), (2) subcutaneous adipose tissue (SAT green) (3) muscle (blue) (4) inter-muscular adipose tissue (IMAT yellow) (5) bone (magenta) (6) miscellaneous intra-pelvic content (e.g.
#OSIRIX LITE SEGMENTATION MANUAL#
Manual segmentation was performed using manual and semi-automated thresholding using the Osirix DICOM viewer (version 6.5.2, by a single operator with 9 years of experience, with images and segmentations audited by a second investigator with 22 years of experience.
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This supra-acetabular level was chosen as it is easily recognizable and contains substantial muscle mass that includes the main hip stabilizers (gluteal, piriformis and iliopsoas muscles), having been used in previous studies of pelvic body composition. In order to evaluate pelvic muscle mass, we selected a standardized single axial image immediately cranial to the acetabular roof. Ground truth labeling (manual segmentation) We hypothesized that the application of a U-net CNN would achieve high accuracy as compared to the reference standard of manual segmentation. The purpose of our study was to develop a deep convolutional neural network (CNN) to automatically segment an axial CT image at a standardized pelvic level for body composition measures. However, no prior studies have evaluated automated systems to measure muscle mass at the pelvis. Recently, fully automated segmentation methods using deep learning techniques have been published for automatic segmentation of abdominal CTs at a lumbar level and MRI at the mid-thigh level. Consequently, the use of valuable body composition parameters for clinical management is limited, underlining the need for reliable automated segmentation methods. Accurate segmentation of different tissues plays a key role in reliably measuring body composition, which is currently done using time-consuming manual or semi-automated methods. Ĭross-sectional imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), are part of the clinical workup for many diseases and enable accurate measurement of body composition. Body composition (amount and distribution of adipose tissue and muscle in the human body) and sarcopenia are increasingly relevant as predictors of overall health, and has been associated with osteoporosis and poor outcomes after surgery, trauma and cancer. Moreover, this process can be seen as part of a more generalized age-related loss of muscle volume and fatty infiltration known as sarcopenia. Atrophy and fatty infiltration of pelvic muscles are associated with pathological conditions such as hip osteoarthritis, neuromuscular disorders and iatrogenic injury during hip replacement surgery. The gluteal, piriformis and iliopsoas muscles play an important role in providing stability and mobility of hips and lower extremities.