Automatic detection and segmentation of crohn's disease tissues from abdominal MRI

IEEE Transactions on Medical Imaging

Volumn 32, Issue 12, 2013, Pages 2332-2347

  Mahapatra, Dwarikanath ^a   Schuffler, Peter J ^a   Tielbeek, Jeroen A W ^b   Makanyanga, Jesica C ^c   Stoker, Jaap ^b   Taylor, Stuart A ^c   Vos, Franciscus M ^b   Buhmann, Joachim M ^a  

^a ETH ZURICH   (Switzerland)

^b ACADEMIC MEDICAL CENTER   (Netherlands)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Context; Crohn's disease (CD); Graph cut; Image features; Probability maps; Random forests; Segmentation; Semantic information; Supervoxels

Indexed keywords

CONTEXT; CROHN'S DISEASE; GRAPH CUT; IMAGE FEATURES; PROBABILITY MAPS; RANDOM FORESTS; SEMANTIC INFORMATION; SUPERVOXELS;

DATA PROCESSING; DECISION TREES; DISEASES; GRAPHIC METHODS; MAGNETIC RESONANCE IMAGING; PROBABILITY; SEMANTICS;

IMAGE SEGMENTATION;

ABDOMINAL RADIOGRAPHY; ALGORITHM; ANISOTROPY; ARTICLE; AUTOMATION; CLASSIFIER; CROHN DISEASE; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; HUMAN; INFORMATION PROCESSING; MACHINE LEARNING; MEASUREMENT ACCURACY; NOISE; NUCLEAR MAGNETIC RESONANCE IMAGING; PROBABILITY; PUNISHMENT; RANDOM FOREST; SEMANTICS; STATISTICS; SUPPORT VECTOR MACHINE;

EID: 84890379719     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2013.2282124     Document Type: Article

References (54)

1. E. Armitage, H.Drummond, and D. W. et al., "Increasing incidence of both juvenile-onset Crohn's disease and ulcerative colitis in Scotland," Eur. J. Gastroenterol. Hepatol., vol. 13, no. 12, pp. 1439-1447, 2001. (Pubitemid 33151496)
2. K. Fonager, H. Sorensen, and J.Olsen, "Change in incidence of Crohn's disease and ulcerative colitis in Denmark:Astudy based on the national registry of patients," Int. J. Epidemiol., vol. 26, no. 5, pp. 1003-1008, 1997. (Pubitemid 27478516)
3. A. Yu, L. C. E. Wu, P. Mulani, and J. Chao, "The costs of Crohn's disease in the United States and other western countries: A systematic review," CurrentMed. Res. Opinion, vol. 24, no. 2, pp. 319-328, 2008. (Pubitemid 351294380)
4. J. Mary and R. Modigliani, "Development and validation of an endoscopic index of the severity for Crohn's disease: A prospective multicentre study," Gut, vol. 30, no. 7, pp. 983-989, 1989.
5. J. Rimola, S. Rodriguez, and O. G. Bosch et al., "Magnetic resonance for assessment of disease activity and severity in ileocolonic Crohn's disease," Gut, vol. 58, pp. 1113-1120, 2009.
6. K. Bodily, J. Fletcher, andC. Solemet al., "Crohn disease:Mural attenuation and thickness at contrast-enhanced CT enterography-correlation with endoscopic and histologic findings of inflammation," Radiology, vol. 238, no. 2, pp. 505-516, 2006. (Pubitemid 43134363)
7. K. Horsthuis, S. Bipat, R. Bennink, and J. Stoker, "Inflammatory bowel disease diagnosed with US, MR, scinitigraphy, and CT" meta-analysis of prospective studies," Radiology, vol. 247, no. 1, pp. 64-79, 2008.
8. F. M. Vos, J. Tielbeek, R. Naziroglu, Z. Li, P. J. Schüffler, D. Mahapatra, A. Wiebel, C. Lavini, J. M. Buhmann, H. Hege, J. Stoker, and L. van Vliet, "Computational modeling for assessment of IBD: To be or not to be?," in Proc. IEEE Eng. Med. Biol. Soc., 2012, pp. 3974-3977.
9. D. Mahapatra, P. J. Schüffler, J. Tielbeek, J. M. Buhmann, and F. M. Vos, "A supervised learning based approach to detect Crohn's disease in abdominal MR volumes," in Proc. MICCAI Workshop Computat. Clin. Appl. Abdominal Imag., 2012, pp. 97-106.
10. D. Mahapatra, P. Schüffler, J. Tielbeek, F. Vos, and J. B., "Crohn's disease tissue segmentation from abdominal MRI using semantic information and graph cuts," in Proc. IEEE Int. Symp. Biomed. Imag., 2013, pp. 358-361.
11. M. Bhushan, J. Schnabel, L. Risser, M. Heinrich, J. M. Brady, and M. Jenkinson, "Motion correction and parameter estimation in DCEMRI sequences: Application to colorectal cancer," in Proc. MICCAI, 2011, pp. 476-483.
12. K. Schunk, "Small bowel magnetic resonance imaging for inflammatory bowel disease," Top. Magn. Reson. Imag., vol. 13, no. 6, pp. 409-425, 2002. (Pubitemid 36009040)
13. S. Atasoy, D. Mateus, A. Meining, G.-Z. Yang, and N. Navab, "Targeted optical biopsies for surveillance endoscopies," in Proc. MICCAI, 2011, pp. 83-90.
14. M. Pereyra, N. Dobigeon, H. Batatia, and J.-Y. Tourneret, "Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model," IEEE Trans. Med. Imag., vol. 31, no. 8, pp. 1509-1520, Aug. 2012.
15. A. Madabhushi and D.Metaxas, "Combining low-, high-level and empirical domain knowledge for automatic segementation of ultrasonic breast lesions," IEEE Trans. Med. Imag., vol. 22, no. 2, pp. 155-169, Feb. 2003.
16. Y. Song, W. Cai, J. Kim, and D. Feng, "A multistage discriminative model for tumot and lymph node detection in thoracic images," IEEE Trans. Med. Imag., vol. 31, no. 5, pp. 1061-1075, May 2012.
17. T. Shepherd, S. Prince, and D. Alexander, "Interactive lesion segmentation with shape priors from offline and online learning," IEE Trans. Med. Imag., vol. 31, no. 9, pp. 1698-1712, Sep. 2012.
18. J. Cheng, D. Tao, D. Wong, and B. Lee et al., "Focal biologically inspired feature for glaucoma type detection," in Proc. MICCAI, 2011, pp. 91-98.
19. D. Mahapatra and Y. Sun, "Registration of dynamic renal MR images using neurobiological model of saliency," in Proc. ISBI, 2008, pp. 1119-1122.
20. D. Mahapatra, M. Saini, and Y. Sun, "Illumination invariant tracking in office environments using neurobiology-saliency based particle filter," in Proc. IEEE Int. Conf. Multimedia Expo, 2008, pp. 953-956.
21. D. Mahapatra and Y. Sun, "Nonrigid registration of dynamic renalMR images using a saliency based MRF model," in Proc. MICCAI, 2008, pp. 771-779.
22. D.Mahapatra and Y. Sun, "MRF based intensity invariant elastic registration of cardiac perfusion images using saliency information," IEEE Trans. Biomed. Eng., vol. 58, no. 4, pp. 991-1000, Apr. 2011.
23. D. Mahapatra and Y. Sun, "Integrating segmentation information for improved MRF-based elastic image registration," IEEE Trans. Image Process., vol. 21, no. 1, pp. 170-183, Jan. 2012.
24. O. Pauly, B.Glocker, A. Criminisi, andD.Mateus, "Fastmultiple organ detection and localization in whole-body MR Dixon sequences," in Proc. MICCAI, 2011, pp. 239-247.
25. B. Kelm, S.Mittal, and Y. Zheng et al., "Detection, grading and classification of coronary stenoses in computed tomography angiography," in Proc. MICCAI, 2011, pp. 25-32.
26. J. Iglesias, J. Jiang, C.-Y.Liu, andZ. Tu, "Classification ofAlzheimer's disease using a self-smoothing operator," in Proc. MICCAI, 2011, pp. 58-65.
27. D. Zhang, Y.Wang, L. Zhou, H. Yuan, and D. Shen, "Multimodal classification of Alzheimer's disease andmild cognitive impairment," Neuroimage, vol. 55, no. 3, pp. 856-867, 2011.
28. S. Bauer, L.-P. Nolte, and M. Reyes, "Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization," in Proc. MICCAI, 2011, pp. 354-361.
29. U. Avni, H. Greenspan, and J. Goldberger, "X-ray categorization and spatial localization of chest pathologies," in Proc. MICCAI, 2011, pp. 199-206.
30. B. Irving, P. Goussard, R.Gie,A. Todd-Pokropek, and P. Taylor, "Identification of paediatric tuberculosis from airway shape features," in Proc. MICCAI, 2011, pp. 133-140.
31. R. Xu, Y. Hirano, R. Tachibana, and S. Kido, "Classification of diffuse lung disease patterns on high-resolution computed tomography by a bag of words approach," in Proc. MICCAI, 2011, pp. 183-190.
32. E. Kirsanova and M. Sadovsky, "Entropy approach in the analysis of anisotropy of digital images," Open Syst. Inf. Dynam., vol. 9, no. 3, pp. 239-250, 2002.
33. Y. Zheng, A. Barbu, B. Beorgescu, M. Scheuering, and D. Comaniciu, "Four chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features," IEEE Trans. Med. Imag., vol. 27, no. 11, pp. 1668-1681, Nov. 2008.
34. C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, "Conditional variable importance for Random forests," BMC Bioinformat., vol. 9, 2008.
35. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001. (Pubitemid 32933532)
36. T. J. Fuchs and J. M. Buhmann, "Computational pathology: Challenges and promises for tissue analysis," Comput. Med. Imag. Graphics, vol. 35, no. 7-8, pp. 515-530, 2011.
37. E. Geremia, B. Menze, O. Clatz, E. Konukgolu, A. Criminsi, and N. Ayache, "Spatial decision forests for MS lesion segmentation in multichannel MR images," in Proc. MICCAI, 2010, pp. 111-118.
38. A. Montillo, J. Shotton, J. Winn, J. Iglesias, D. Metaxas, and A. Criminisi, "Entangled decision forests and their application for semantic segmentation of CT images," in Proc. Int Conf. Inf. Process. Med. Imag., 2011, pp. 184-196.
39. M. Berks, Z. Chen, S. Astley, and C. Taylor, "Detecting and classifying linear structures in mammograms using random forests," in Proc. Int Conf. Inf. Process. Med. Imag., 2011, pp. 510-524.
40. B. Julesz, E. Gilbert, L. Shepp, and H. Frisch, "Inability of humans to discriminate between visual textures that agree in second-order statistics- revisited," Perception, vol. 2, no. 4, pp. 391-405, 1973.
41. M. Petrou, V. Kovalev, and J. Reichenbach, "Three-dimensional nonlinear invisible boundary detection," IEEE Trans. Image Process., vol. 15, no. 10, pp. 3020-3032, Oct. 2006. (Pubitemid 44473644)
42. R. L. D. Valois, D. G. Albrecht, and L. G. Thorell, "Spatial- frequency selectivity of cells in macaque visual cortex," Vis. Res., vol. 22, no. 5, pp. 545-559, 1982. (Pubitemid 12155596)
43. V. Kovalev, M. Petrou, and Y. Bondar, "Texture anisotropy in 3-D images," IEEE Trans. Image Process., vol. 8, no. 3, pp. 346-360, Mar. 1999.
44. [Online]. Available: http://www.cs.ucl.ac.uk/staff/S.Arridge/teaching/ ndsp/
45. Z. Tu and X. Bai, "Auto-context and its application to high-level vision tasks and 3-D brain image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 10, pp. 1744-1757, Oct. 2010.
46. W. Li, S. Liao, Q. Feng, W. Chen, and D. Shen, "Learning image context for segmentation of prostate in CT-guided radiotherapy," in Proc. MICCAI, 2011, pp. 570-578.
47. D.Mahapatra, "Cardiac LV and RV segmentation using mutual context information," in Proc. MICCAI-MLMI, 2012, pp. 201-209.
48. A. Criminsi, J. Shotton, and S. Bucciarelli, "Decision forests with long range spatial context for organ localization," in Proc. MICCAI Workshop Probabil. Models Med. Image Anal., 2009.
49. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Patt. Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
50. C. Xu and J. J. Corso, "Evaluation of super-voxel methods for early video processing," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 1202-1209.
51. M. Cohen, R. Dubois, and M. Zeineh, "Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging," Human Brain Mapp., vol. 10, no. 4, pp. 204-211, 2000. (Pubitemid 30618504)
52. Y. Boykov and O. Veksler, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001. (Pubitemid 33137959)
53. A. Liaw and M. Wiener, "Classification and regression by randomforest," R News vol. 2, no. 3, pp. 18-22, 2002.
54. Sorensen-Dicecoefficient [Online]. Available: http://en.wikipedia.org/ wiki/Sorensen-Dicecoefficient