Abstract
This paper follows previous works on the construction of interactive medical image segmentation system, allowing quick volume segmentation requiring minimal intervention of the human operator. This paper contributes to tackle this problem enhancing the previously proposed Active Learning image segmentation system with Domain Knowledge. Active Learning iterates the following process: first, a classifier is trained on the basis of a set of image features extrated for each training labeled voxel; second, a human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set assigining corresponding label. Finally, image segmentation is produced by voxel classification of the entire volume with the resulting classifier. The approach has been applied to the segmentation of the thrombus in CTA data of Abdominal Aortic Aneurysm (AAA) patients. The Domain Knowledge referring to the expected shape of the target structures is used to filter out undesired region detections in a post-processing step. We report computational experiments over 6 abdominal CTA datasets consisting. The performance measure is the true positive rate (TPR). Surface rendering provides a 3D visualization of the segmented thrombus. A few Active Learning iterations achieve accurate segmentation in areas where it is difficult to distinguish the anatomical structures due to noise conditions and similarity of gray levels between the thrombus and other structures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Settles, B.: Active learning literature survey. Sciences New York 15(2) (2010)
Lempitsky, V., Verhoek, M., Alison Noble, J., Blake, A.: Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 447–456. Springer, Heidelberg (2009)
Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel MR images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 111–118. Springer, Heidelberg (2010)
Yi, Z., Criminisi, A., Shotton, J., Blake, A.: Discriminative, semantic segmentation of brain tissue in MR images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 558–565. Springer, Heidelberg (2009)
Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in ct volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis (2009)
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
Maiora, J., Graña, M.: Abdominal cta image analisys through active learning and decision random forests: Aplication to AAA segmentation. In: The 2012 International Joint Conference on Neural Networks, IJCNN, pp. 1–7 (2012)
Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3d vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis 13(6), 819–845 (2009)
Macia, I., Grana, M., Maiora, J., Paloc, C., de Blas, M.: Detection of type ii endoleaks in abdominal aortic aneurysms after endovascular repair. Computers in Biology and Medicine 41(10), 871–880 (2011)
Macia, I., Graña, M., Paloc, C.: Knowledge management in image-based analysis of blood vessel structures. Knowledge and Information Systems 30(2), 457–491 (2012)
de Bruijne, M., van Ginneken, B., Viergever, M.A., Niessen, W.J.: Interactive segmentation of abdominal aortic aneurysms in cta images. Med. Image Anal. 8(2), 127–138 (2004)
Olabarriaga, S., Rouet, J., Fradkin, M., Breeuwer, M., Niessen, W.: Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling. IEEE Transactions on Medical Imaging 24(4), 477–485 (2005)
Zhuge, F., Rubin, G.D., Sun, S.H., Napel, S.: An abdominal aortic aneurysm segmentation method: Level set with region and statistical information. Medical Physics 33(5), 1440–1453 (2006)
Demirci, S., Lejeune, G., Navab, N.: Hybrid deformable model for aneurysm segmentation. In: ISBI 2009, pp. 33–36 (2009)
Freiman, M., Esses, S.J., Joskowicz, L., Sosna, J.: An Iterative Model-Constraint Graph-cut Algorithm for Abdominal Aortic Aneurysm Thrombus Segmentation. In: Proc. of the 2010 IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, ISBI 2010, Rotterdam, The Netherlands, pp. 672–675. IEEE (April 2010)
Chyzhyk, D., Ayerdi, B., Maiora, J.: Active learning with bootstrapped dendritic classifier applied to medical image segmentation. Pattern Recognition Letters (online, 2013)
Maiora, J., Ayerdi, B., Graña, M.: Random forest active learning for computed tomography angiography image segmentation. Neurocomputing (inpress, 2013)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9(7), 1545–1588 (1997)
Ho, T.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Barandiaran, I., Paloc, C., Graña, M.: Real-time optical markerless tracking for augmented reality applications. Journal of Real-Time Image Processing 5, 129–138 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ayerdi, B., Maiora, J., Graña, M. (2013). Enhancing Active Learning Computed Tomography Image Segmentation with Domain Knowledge. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)