We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 + - 0.016 (mean + - std) and an average symmetric surface distance (ASD) of 0.994 + - 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 + - 0.031 DC and 1.556 + - 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an “eyeball test” to assess whether patients will tolerate major surgery or chemotherapy, “eyeballing” is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts. In this study, we propose a fully automated deep learning system for the segmentation of skelet al muscle cross-sectional area (CSA) on an axial computed tomography image taken at the third lumbar vertebra. We utilized a fully automated deep segmentation model derived from an extended implementation of a fully convolutional network with weight initialization of an ImageNet pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis. This experiment was conducted by varying window level (WL), window width (WW), and bit resolutions in order to better understand the effects of the parameters on the model performance. Our best model, fine-tuned on 250 training images and ground truth labels, achieves 0.93 ± 0.02 Dice similarity coefficient (DSC) and 3.68 ± 2.29 difference between predicted and ground truth muscle CSA on 150 held-out test cases. Ultimately, the fully automated segmentation system can be embedded into the clinical environment to accelerate the quantification of muscle and expanded to volume analysis of 3D datasets. Purpose To determine age- and gender-dependent whole-body adipose tissue and muscle volumes in healthy Swiss volunteers in Dixon MRI in comparison with anthropometric and bioelectrical impedance (BIA) measurements. Methods Fat–water-separated whole-body 3 Tesla MRI of 80 healthy volunteers (ages 20 to 62 years) with a body mass index (BMI) of 17.5 to 26.2 kg m2 (10 men, 10 women per decade). Age and gender-dependent volumes of total adipose tissue (TAT), visceral adipose tissue (VAT), total abdominal subcutaneous adipose tissue (ASAT) and total abdominal adipose tissue (TAAT), and the total lean muscle tissue (TLMT) normalized for body height were determined by semi-automatic segmentation, and correlated with anthropometric and BIA measurements as well as lifestyle parameters. Results The TAT, ASAT, VAT, and TLMT indexes (TATi, ASATi, VATi, and TLMTi, respectively) (L m2 ± standard deviation) for women men were 6.4 ± 1.8 5.3 ± 1.7, 1.6 ± 0.7 1.2 ± 0.5, 0.4 ± 0.2 0.8 ± 0.5, and 5.6 ± 0.6 7.1 ± 0.7, respectively. The TATi correlated strongly with ASATi (r > 0.93), VATi, BMI and BIA (r > 0.70), and TAATi (r > 0.96), and weak with TLMTi for both genders (r > –0.34). The VAT was the only parameter showing an age dependency (r > 0.32). The BMI and BIA showed strong correlation with all MR-derived adipose tissue volumes. The TAT mass was estimated significantly lower from BIA than from MRI (both genders P < .001; mean bias –5 kg). Conclusions The reported gender-specific MRI-based adipose tissue and muscle volumes might serve as normative values. The estimation of adipose tissue volumes was significantly lower from anthropometric and BIA measurements than from MRI. Magn Reson Med 79:449–458, 2018. © 2017 International Society for Magnetic Resonance in Medicine. PurposeTo develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skelet al muscle volume.Materials and MethodsThe metho ... Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al (2004), Klein, et al (2005), and Heckemann, et al (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003–2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.