Leveraging mid-level semantic boundary cues for automated lymph node detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9350, Issue , 2015, Pages 53-61

  Seff, Ari ^a   Lu, Le ^a   Barbu, Adrian ^a   Roth, Holger ^a   Shin, Hoo Chang ^a   Summers, Ronald M ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BODY FLUIDS; COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; MEDICAL COMPUTING; NEURAL NETWORKS; SEMANTICS;

COMPUTER AIDED DETECTION; COMPUTER AIDED DIAGNOSIS SYSTEMS; CONVOLUTIONAL NEURAL NETWORK; HISTOGRAMS OF ORIENTED GRADIENTS (HOG); IMAGE TRANSFORMATIONS; LYMPH NODE DETECTIONS; ROBUST STATISTICS; SEMANTIC BOUNDARY;

MEDICAL IMAGING;

EID: 84951138738     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24571-3_7     Document Type: Conference Paper

References (16)

1. Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S.K., Comaniciu, D.: Automatic detection and segmentation of lymph nodes from CT data. IEEE Trans. Med. Imaging 31(2), 240–250 (2012)
2. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)
4. Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proc. BMVC, pp. 1–11 (2009). doi:10.5244/C.23.91
5. Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV, pp. 1841–1848. IEEE (2013)
6. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
7. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. on Pat. Ana. and Mach. Intell. 32(9), 1627–1645 (2010)
8. Feulner, J., Zhou, S.K., Hammon, M., Hornegger, J., Comaniciu, D.: Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior. Medical Image Analysis 17(2), 254–270 (2013)
9. Lim, J.J., Zitnick, C.L., Dollár, P.: Sketch tokens: A learned mid-level representation for contour and object detection. In: CVPR, pp. 3158–3165. IEEE (2013)
10. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CoRR abs/1411.4038 (2014)
11. Lu, L., Bi, J. Wolf, M., Salganicoff, M.: Effective 3D object detection and regression using probabilistic segmentation features in ct images. In: CVPR, pp. 1049–1056. IEEE (2011)
12. Roth, H., et al.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 520–527. Springer, Heidelberg (2014)
13. Schwartz, L., Bogaerts, J., Ford, R., Shankar, L., Therasse, P., Gwyther, S., Eisenhauer, E.A.: Evaluation of lymph nodes with recist 1.1. Euro. J. of Cancer 45(2), 261–267 (2009)
14. Seff, A., et al.: 2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 544–552. Springer, Heidelberg (2014)
15. Summers, R.M., Yao, J., Pickhardt, P.J., Franaszek, M., Bitter, I., Brickman, D., Krishna, V., Choi, J.R.: Computed tomographic virtual colonoscopy computeraided polyp detection in a screening population. Gastroenterology 129, 1832–1844 (2005)
16. Tola, E., Lepetit, V., Fua, P.: DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo. IEEE Trans. on Pat. Ana. and Mach. Intell. 32(5), 815–830 (2010)