A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

Computer Methods and Programs in Biomedicine

Volumn 144, Issue , 2017, Pages 97-104

  Wang, Yunzhi ^a   Qiu, Yuchen ^a   Thai, Theresa ^b   Moore, Kathleen ^b   Liu, Hong ^a   Zheng, Bin ^a  

^a University of Oklahoma   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Computer aided detection (CAD); Convolution neural network (CNN); Deep learning; Segmentation of adipose tissue; Subcutaneous fat area (SFA); Visceral fat area (VFA)

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; COMPUTER NETWORKS; CONVOLUTION; DEEP LEARNING; DIAGNOSIS; IMAGE SEGMENTATION; NEURAL NETWORKS; PIXELS; RISK ASSESSMENT; STATISTICAL TESTS; TISSUE;

ADIPOSE TISSUE; COMPUTER-AIDED DETECTION; CONVOLUTION NEURAL NETWORK; SUBCUTANEOUS FAT; VISCERAL FAT;

COMPUTERIZED TOMOGRAPHY;

ADIPOSE TISSUE; ARTICLE; AUTOMATION; BODY SIZE; CANCER PATIENT; CLASSIFIER; CLINICAL ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER AIDED DESIGN; CONE BEAM COMPUTED TOMOGRAPHY; CONVOLUTION NEURAL NETWORK; FEMALE; HUMAN; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; INTRAPERITONEAL FAT; MACHINE LEARNING; OVARY CANCER; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; SUBCUTANEOUS FAT; ABDOMEN; ABDOMINAL FAT; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; INTRAABDOMINAL FAT; X-RAY COMPUTED TOMOGRAPHY;

ABDOMEN; ABDOMINAL FAT; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INTRA-ABDOMINAL FAT; NEURAL NETWORKS (COMPUTER); SUBCUTANEOUS FAT; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85015984007     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2017.03.017     Document Type: Article

References (35)

1. [1] Ogden, C.L., Carroll, M.D., Kit, B.K., Flegal, K.M., Prevalence of childhood and adult obesity in the United States, 2011-2012. Jama 311 (2014), 806–814.
2. [2] Kissebah, A.H., VYDELINGUM, N., MURRAY, R., EVANS, D.J., KALKHOFF, R.K., ADAMS, P.W., Relation of body fat distribution to metabolic complications of obesity. J. Clin. Endocrinol. Metabolism 54 (1982), 254–260.
3. [3] Mun, E.C., Blackburn, G.L., Matthews, J.B., Current status of medical and surgical therapy for obesity. Gastroenterology 120 (2001), 669–681.
4. [4] Després, J.P., Lemieux, I., Bergeron, J., Pibarot, P., Mathieu, P., Larose, E., Rodés-Cabau, J., Bertrand, O.F., Poirier, P., Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler. Thromb. Vasc. Biol. 28 (2008), 1039–1049.
5. [5] Makrogiannis, S., Caturegli, G., Davatzikos, C., Ferrucci, L., Computer-aided assessment of regional abdominal fat with food residue removal in CT. Acad. Radiol. 20 (2013), 1413–1421.
6. [6] Guiu, B., Petit, J.M., Bonnetain, F., Ladoire, S., Guiu, S., Cercueil, J.-P., Krausé, D., Hillon, P., Borg, C., Chauffert, B., Visceral fat area is an independent predictive biomarker of outcome after first-line bevacizumab-based treatment in metastatic colorectal cancer. Gut 59 (2010), 341–347.
7. [7] Slaughter, K.N., Thai, T., Penaroza, S., Benbrook, D.M., Thavathiru, E., Ding, K., Nelson, T., McMeekin, D.S., Moore, K.N., Measurements of adiposity as clinical biomarkers for first-line bevacizumab-based chemotherapy in epithelial ovarian cancer. Gynecol. Oncol. 133 (2014), 11–15.
8. [8] Wang, Y., Thai, T., Moore, K., Ding, K., Mcmeekin, S., Liu, H., Zheng, B., Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients. Oncol. Lett. 12 (2016), 680–686.
9. [9] Yoshizumi, T., Nakamura, T., Yamane, M., Waliul Islam, A.H.M., Menju, M., Yamasaki, K., Arai, T., Kotani, K., Funahashi, T., Yamashita, S., Abdominal fat: standardized technique for measurement at CT. Radiology 211 (1999), 283–286.
10. [10] Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., Zheng, B., Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome. BMC Med. Imag., 16, 2016, 52, 10.1186/s12880-016-157-5.
11. [11] Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 2012, 1097–1105.
12. [12] Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D., Face recognition: a convolutional neural-network approach. Neural Netw. IEEE Trans. 8 (1997), 98–113.
13. [13] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86 (1998), 2278–2324.
14. [14] Simard, P.Y., Steinkraus, D., Platt, J.C., Best practices for convolutional neural networks applied to visual document analysis. IEEE, 2003, 958.
15. [15] Bengio, Y., Learning deep architectures for AI. Found. Trends Mach. Learn. 2 (2009), 1–127.
16. [16] Jones, N., Computer science: the learning machines. Nature 505 (2014), 146–148.
17. [17] Brebisson, A., Montana, G., Deep neural networks for anatomical brain segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, 20–28.
18. [18] Cernazanu-Glavan, C., Holban, S., Segmentation of bone structure in X-ray images using convolutional neural network. Adv. Electr. Comput. Eng 13 (2013), 87–94.
19. [19] Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J., Mitosis detection in breast cancer histology images with deep neural networks. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, 2013, Springer, 411–418.
20. [20] Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H., Brain tumor segmentation with deep neural networks. Med. Image Anal. 35 (2017), 18–31.
21. [21] Dubrovina, A., Kisilev, P., Ginsburg, B., Hashoul, S., Kimmel, R., Computational mammography using deep neural networks. Comput. Methods Biomech. Biomed. Eng.: Imaging & Visualization, 2016, 1–5.
22. [22] Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., Summers, R.M., Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, 2015, Springer, 556–564.
23. [23] Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imag. 35 (2016), 1285–1298.
24. [24] Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D.N., Zhou, X.S., Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans. Med. Imag. 35 (2016), 1332–1343.
25. [25] Chung, H., Cobzas, D., Birdsell, L., Lieffers, J., Baracos, V., Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis, SPIE medical imaging. Int. Soc. Opt. Photon., 2009 72610K-72610K-72618.
26. [26] Hussein, S., Green, A., Watane, A., Papadakis, G., Osman, M., Bagci, U., Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes. 2015 arXiv preprint arXiv:1512.04958.
27. [27] Kim, Y.J., Lee, S.H., Kim, T.Y., Park, J.Y., Choi, S.H., Kim, K.G., Body fat assessment method using CT images with separation mask algorithm. J. Digit. Imag. 26 (2013), 155–162.
28. [28] Mensink, S.D., Spliethoff, J.W., Belder, R., Klaase, J.M., Bezooijen, R., Slump, C.H., Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans, SPIE medical imaging. Int. Soc. Opt. Photon., 2011 79632Q-79632Q-79612.
29. [29] Romero, D., Ramirez, J.C., Mármol, A., Quanification of subcutaneous and visceral adipose tissue using CT. Medical Measurement and Applications, 2006. MeMea 2006. IEEE International Workshop on, 2006, IEEE, 128–133.
30. [30] Zhao, B., Colville, J., Kalaigian, J., Curran, S., Jiang, L., Kijewski, P., Schwartz, L.H., Automated quantification of body fat distribution on volumetric computed tomography. J. Comput. Assist. Tomogr. 30 (2006), 777–783.
31. [31] Leader, J.K., Zheng, B., Rogers, R.M., Sciurba, F.C., Perez, A., Chapman, B.E., Patel, S., Fuhrman, C.R., Gur, D., Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme 1. Acad. Radiol. 10 (2003), 1224–1236.
32. [32] Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y., Theano: a CPU and GPU math expression compiler. Proceedings of the Python for scientific computing conference (SciPy), Austin, TX, 2010, 3.
33. [33] Aghaei, F., Tan, M., Hollingsworth, A.B., Qian, W., Liu, H., Zheng, B., Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Med. Phys. 42 (2015), 6520–6528.
34. [34] Emaminejad, N., Qian, W., Guan, Y., Tan, M., Qiu, Y., Liu, H., Zheng, B., Fusion of quantitative image features and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE. Trans. Biomed. Eng. 63 (2016), 1034–1043.
35. [35] Qiu, Y., Tan, M., McMeekin, S., Thai, T., Ding, K., Moore, K., Liu, H., Zheng, B., Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis. Acta. Radiol. 57 (2016), 1149–1155.