Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis
暂无分享,去创建一个
Georg Fuchs | Hyunkwang Lee | Synho Do | Shahein H. Tajmir | Florian J. Fintelmann | Fabian M. Troschel | Shahein Tajmir | Julia Mario | Hyunkwang Lee | Synho Do | F. Fintelmann | Julia Mario | Georg Fuchs
[1] Albertus Beishuizen,et al. Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients , 2014, Critical Care.
[2] Dana Cobzas,et al. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis , 2009, Medical Imaging.
[3] Marios Anthimopoulos,et al. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.
[4] Ronald M. Summers,et al. Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .
[5] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Binsheng Zhao,et al. Automated Quantification of Body Fat Distribution on Volumetric Computed Tomography , 2006, Journal of computer assisted tomography.
[7] Jenny Lee,et al. Fully Automated Deep Learning System for Bone Age Assessment , 2017, Journal of Digital Imaging.
[8] Douglas E Schaubel,et al. Sarcopenia and mortality after liver transplantation. , 2010, Journal of the American College of Surgeons.
[9] Stanley Heshka,et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. , 2004, Journal of applied physiology.
[10] Subhransu Maji,et al. Deep filter banks for texture recognition and segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Xiangrong Zhou,et al. Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study , 2011, Radiological Physics and Technology.
[12] Victor Alves,et al. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.
[13] Leon Lenchik,et al. Sarcopenia: Current Concepts and Imaging Implications. , 2015, AJR. American journal of roentgenology.
[14] Tony Reiman,et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. , 2008, The Lancet. Oncology.
[15] Tom Kimpe,et al. Increasing the Number of Gray Shades in Medical Display Systems—How Much is Enough? , 2007, Journal of Digital Imaging.
[16] Christopher Joseph Pal,et al. Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..
[17] Krzysztof Krawiec,et al. Segmenting Retinal Blood Vessels With Deep Neural Networks , 2016, IEEE Transactions on Medical Imaging.
[18] Geoffrey Glassock,et al. Eighth International Conference , 2008 .
[19] Ronald M. Summers,et al. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation , 2015, MICCAI.
[20] Eileen Bulger,et al. Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients , 2013, Critical Care.
[21] R. Yokoyama,et al. Automated recognition of the psoas major muscles on X-ray CT images , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[22] M. Patlas,et al. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. , 2012, The British journal of radiology.
[23] Martin Jägersand,et al. FEM-based automatic segmentation of muscle and fat tissues from thoracic CT images , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.
[24] Lina J. Karam,et al. Understanding how image quality affects deep neural networks , 2016, 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX).
[25] M. Braga,et al. Effect of sarcopenia and visceral obesity on mortality and pancreatic fistula following pancreatic cancer surgery , 2016, The British journal of surgery.
[26] P. Thaker,et al. Pre-operative Assessment of Muscle Mass to Predict Surgical Complications and Prognosis in Patients With Endometrial Cancer , 2014, Annals of Surgical Oncology.
[27] Daniel F Polan,et al. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study , 2016, Physics in medicine and biology.
[28] B. Lewis,et al. Image quality in obese patients undergoing 256-row computed tomography coronary angiography , 2012, The International Journal of Cardiovascular Imaging.
[29] Martin Jägersand,et al. Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle , 2016, IEEE Transactions on Medical Imaging.
[30] Marian A E de van der Schueren,et al. Loss of Muscle Mass During Chemotherapy Is Predictive for Poor Survival of Patients With Metastatic Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[31] Paul F. Whelan,et al. Using filter banks in Convolutional Neural Networks for texture classification , 2016, Pattern Recognit. Lett..
[32] Yaozong Gao,et al. Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests , 2016, IEEE Transactions on Medical Imaging.
[33] Xiangrong Zhou,et al. Automated segmentation of recuts abdominis muscle using shape model in X-ray CT images , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[34] S B Heymsfield,et al. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. , 1998, Journal of applied physiology.
[35] Eddy S Yang,et al. CT Measures of Bone Mineral Density and Muscle Mass Can Be Used to Predict Noncancer Death in Men with Prostate Cancer. , 2017, Radiology.
[36] Max A. Viergever,et al. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.
[37] Geerard L Beets,et al. Functional compromise reflected by sarcopenia, frailty, and nutritional depletion predicts adverse postoperative outcome after colorectal cancer surgery. , 2015, Annals of surgery.
[38] L. Mccargar,et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.