Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

Hemke, Robert; Buckless, Colleen; Tsao, Andrew; Wang, Benjamin; Torriani, Martin

doi:10.1007/s00256-019-03289-8

Cited by 67 publications

(51 citation statements)

References 30 publications

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“…More recently, deep learning and CNN techniques have been used to provide an automatic quantification of muscle fatty infiltration in neck muscles from MR images [17] or abdominal muscles from CT datasets [27,28]. Both studies reported good agreement between the automatic approach and human raters.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

et al. 2020

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Objectives This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. Methods One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Results Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good-very good estimates of muscle atrophy (R 2 = 0.87), fatty infiltration (R 2 = 0.91), and overall muscle degeneration (R 2 = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R 2 = 0.61) than human raters (R 2 = 0.87). Conclusions Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. Key Points • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters.

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Section: Discussionmentioning

confidence: 99%

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

et al. 2020

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“…Deep learning techniques are emerging to support diagnoses with high accuracy, enhancing the speed of image interpretation and, thus, improve the clinical efficiency for a wide range of medical tasks. For example, recent studies showed improved and accurate body composition on CT [ 14 , 15 ]. For muscle mass measurement, recent studies created and validated automated segmentation of the abdominal muscle on manually extracted CT-image at the L3 level [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

Ackermans

Volmer

Wee

et al. 2021

Sensors

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Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

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“…The deep CNN approach has the ability to achieve high accuracy when compared to manual segmentations, by a specialist, as the reference standard. [5][6][7] Such works are gaining scientific attention due to the fact that automatic segmentation of skeletal muscle on CT scans can be challenging because of the high variability between individuals. There are several factors that contribute to the variability of CT images.…”

Section: Introductionmentioning

confidence: 99%

Abdominal muscle segmentation from CT using a convolutional neural network

Edwards

Chhabra

Dormer

et al. 2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

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CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient's course of treatment. In order to measure the muscle, radiologists must segment and review each CT slice manually, which is a time-consuming task. In this work, we present a fully convolutional neural network (CNN) for the segmentation of abdominal muscle on CT. We achieved a mean Dice similarity coefficient of 0.92, a mean precision of 0.93, and a mean recall of 0.91 in an independent test set. The CNN-based segmentation method can provide an automatic tool for the segmentation of abdominal muscle. As a result, the time required to obtain information about changes in abdominal muscle using the CNN takes a fraction of the time associated with manual segmentation methods and thus can provide a useful tool in the clinical application.

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Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

Cited by 67 publications

References 30 publications

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

Abdominal muscle segmentation from CT using a convolutional neural network

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