Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-11-2017 | Original Article

Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures

Authors: Paola Casti, Arianna Mencattini, Marcello H. Nogueira-Barbosa, Lucas Frighetto-Pereira, Paulo Mazzoncini Azevedo-Marques, Eugenio Martinelli, Corrado Di Natale

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2017

Abstract

Purpose

In clinical practice, the constructive consultation among experts improves the reliability of the diagnosis and leads to the definition of the treatment plan for the patient. Aggregation of the different opinions collected by many experts can be performed at the level of patient information, abnormality delineation, or final assessment.

Methods

In this study, we present a novel cooperative strategy that exploits the dynamic contribution of the classification models composing the ensemble to make the final class assignment. As a proof of concept, we applied the proposed approach to the assessment of malignant infiltration in 103 vertebral compression fractures in magnetic resonance images.

Results

The results obtained with repeated random subsampling and receiver operating characteristic analysis indicate that the cooperative system statistically improved (\(p<0.01\)) the classification accuracy of individual modules as well as of that based on the manual segmentation of the fractures provided by the experts.

Conclusions

The performances have been also compared with those obtained with those of standard ensemble classification algorithms showing superior results.

Rokach L (2010) Pattern classification using ensemble methods, vol 75. World Scientific, Singapore

Dastgheib ZA, Pouya OR, Lithgow B, Moussavi Z (2016) Comparison of a new ad-hoc classification method with support vector machine and ensemble classifiers for the diagnosis of Meniere’s disease using EVestG signals. In: 2016 IEEE Canadian conference on electrical and computer engineering (CCECE). IEEE, pp 1–4

Da Silva LA, Hernandez EDM, Rangayyan RM (2008) ’Classification of breast masses using a committee machine of artificial neural networks. J Electron Imaging 17(1):013017CrossRef

Kuncheva LI (2012) Switching between selection and fusion in combining classifiers: an experiment. IEEE Trans Syst Man Cybern B 32(2):146–156CrossRef

Antunes S, Esposito A, Palmisano A, Colantoni C, Cerutti S, Rizzo G (2016) Cardiac multi-detector CT segmentation based on multiscale directional edge detector and 3D level set. Ann Biomed Eng 44(5):1487–1501CrossRefPubMed

Zhao Y, Rada L, Chen K, Harding SP, Zheng Y (2015) Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans Med Imaging 34(9):1797–1807CrossRefPubMed

Siefert AW, Icenogle DA, Rabbah JPM, Saikrishnan N, Rossignac J, Lerakis S, Yoganathan AP (2013) Accuracy of a mitral valve segmentation method using J-splines for real-time 3D echocardiography data. Ann Biomed Eng 41(6):1258–1268CrossRefPubMedPubMedCentral

Guliato D, Rangayyan RM, Carnielli WA, Desautels JL (2003) Fuzzy fusion operators to combine results of complementary medical image segmentation techniques. J Electron Imaging 12(3):379–389CrossRef

He R, Sajja BR, Datta S, Narayana PA (2008) Volume and shape in feature space on adaptive FCM in MRI segmentation. Ann Biomed Eng 36(9):1580–1593CrossRefPubMedPubMedCentral

10.

Melkemi KE, Batouche M, Foufou S (2006) A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics. Pattern Recognit Lett 27(11):1230–1238CrossRef

11.

Benamrane N, Nassane S (2007) Medical image segmentation by a multi-agent system approach. In: Multiagent system technologies. Springer, Berlin, pp 49–60

12.

Bovenkamp EG, Dijkstra J, Bosch JG, Reiber JH (2009) User-agent cooperation in multiagent IVUS image segmentation. IEEE Trans Med Imaging 28(1):94–105CrossRefPubMed

13.

Chen X, Udupa JK, Bagci U, Zhuge Y, Yao J (2012) Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans Image Process 21(4):2035–2046CrossRefPubMedPubMedCentral

14.

Lê M, Unkelbach J, Ayache N, Delingette H (2016) Sampling image segmentations for uncertainty quantification. Med Image Anal 34:42–51CrossRefPubMed

15.

Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903–921CrossRefPubMedPubMedCentral

16.

Kohlberger T, Singh V, Alvino C, Bahlmann C, Grady L (2012) Evaluating segmentation error without ground truth. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 528–536

17.

Casti P, Mencattini A, Salmeri M, Ancona A, Mangeri F, Pepe ML, Rangayyan RM (2016) Contour-independent detection and classification of mammographic lesions. Biomed Signal Process Control 25:165–177CrossRef

18.

Martinelli E, Magna G, Vergara A, Di Natale C (2014) Cooperative classifiers for reconfigurable sensor arrays. Sens Actuator B Chem 199:83–92CrossRef

19.

Magna G, Casti P, Jayaraman SV, Salmeri M, Mencattini A, Martinelli E, Di Natale C (2016) Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system. Knowl Based Syst 101:60–70CrossRef

20.

Brejl M, Sonka M (2000) Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples. IEEE Trans Med Imaging 19(10):973–985CrossRefPubMed

21.

Peng Z, Zhong J, Wee W, Lee JH (2006) Automated vertebra detection and segmentation from the whole spine MR images. In: Proceedings of IEEE EMBS, pp 2527–2530

22.

Huang SH, Chu YH, Lai SH, Novak CL (2009) Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Trans Med Imaging 28(10):1595–1605CrossRefPubMed

23.

Kelm BM, Wels M, Zhou SK, Seifert S, Suehling M, Zheng Y, Comaniciu D (2013) Spine detection in CT and MR using iterated marginal space learning. Med Image Anal 17(8):1283–1292CrossRef

24.

Barbieri PD, Pedrosa GV, Traina AJM, Nogueira-Barbosa MH (2015) Vertebral body segmentation of spine MR images using superpixels. In: Proceedings of IEEE CBMS

25.

Frighetto-Pereira L, Rangayyan RM, Metzner GA, de Azevedo-Marques PM, Nogueira-Barbosa MH (2016) Shape, texture, and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Comput Biol Med 73(1):147–156CrossRefPubMed

26.

Pizer S, Amburn E, Austin J, Cromartie AR, Geselowitz A, Greer T, Romeny BTH, Zimmerman JB, Zuiderveld K (1987) Adaptative histogram equalization and its varations. Comput Vis Graph Image Process 39:355–368CrossRef

27.

Zhao Y, Liu Y, Wu X, Harding SP, Zheng Y (2015) Correction: retinal vessel segmentation: an efficient graph cut approach with retinex and local phase. PLoS ONE 10(4):e0127486CrossRefPubMedPubMedCentral

28.

Rosenfeld A, Kak A (1982) Digital picture processing, vol 2, 2nd edn. Academic Press, New York

29.

Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Proc 10(2):266–277CrossRef

30.

Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRef

31.

Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRef

32.

Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6):641–647CrossRef

33.

Rangayyan RM (2005) Biomedical image analysis. CRC Press, Boca Raton

34.

Davies ER (2004) Machine vision: theory, algorithms, practicalities. Elsevier, Amsterdam

35.

Weinstein RS, Majumdar S (1994) Fractal geometry and vertebral compression fractures. J Bone Miner Res 9(1):1797–1802PubMed

36.

Draper NR, Smith H (1998) Regression analysis. Wiley-Interscience, Hoboken

37.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefPubMed

Title: Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures
Authors: Paola Casti
Arianna Mencattini
Marcello H. Nogueira-Barbosa
Lucas Frighetto-Pereira
Paulo Mazzoncini Azevedo-Marques
Eugenio Martinelli
Corrado Di Natale
Publication date: 01-11-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 11/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1625-2

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 11/2017

Fabricating biomedical origami: a state-of-the-art review

Atlas-Based Segmentation of Temporal Bone Anatomy

Automatic seed picking for brachytherapy postimplant validation with 3D CT images

Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Intravascular endoscopy improvement through narrow-band imaging