Abstract
Modern pathology broadly searches for biomarkers which are predictive for the survival of patients or the progression of cancer. Due to the lack of robust analysis algorithms this work is still performed manually by estimating staining on whole slides or tissue microarrays (TMA). Therefore, the design of decision support systems which can automate cancer diagnosis as well as objectify it pose a highly challenging problem for the medical imaging community.
In this paper we propose Relational Detection Forests (RDF) as a novel object detection algorithm, which can be applied in an off-the-shelf manner to a large variety of tasks. The contributions of this work are twofold: (i) we describe a feature set which is able to capture shape information as well as local context. Furthermore, the feature set is guaranteed to be generally applicable due to its high flexibility. (ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner. Contrary to classical approaches, subspaces are not split based on thresholds but by learning relations between features.
The algorithm is validated on tissue from 133 human clear cell renal cell carcinoma patients (ccRCC) and on murine liver samples of eight mice. On both species RDFs compared favorably to state of the art methods and approaches the detection accuracy of trained pathologists.
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References
Yang, L., Meer, P., Foran, D.J.: Unsupervised segmentation based on robust estimation and color active contour models. IEEE Transactions on Information Technology in Biomedicine 9, 475–486 (2005)
Mertz, K.D., Demichelis, F., Kim, R.,, Schraml, P., Storz, M., Diener, P.-A., Moch, H., Rubin, M.A.: Automated immunofluorescence analysis defines microvessel area as a prognostic parameter in clear cell renal cell cancer. Human Pathology 38, 1454–1462 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features (2001)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Washington, DC, USA, vol. 1, pp. 886–893. IEEE Computer Society, Los Alamitos (2005)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156 (1996)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 63, 3–42 (2006)
Strobl, C., Thomas Augustin, A.L.B.: Unbiased split selection for classification trees based on the gini index. Computational Statistics & Data Analysis 52, 483–501 (2007)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2009) ISBN 3-900051-07-0
Hall, B., Chen, W., Reiss, M., Foran, D.J.: A clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 287–294. Springer, Heidelberg (2007)
Yang, L., Chen, W., Meer, P., Salaru, G., Feldman, M.D., Foran, D.J.: High throughput analysis of breast cancer specimens on the grid. Med. Image Comput. Comput. Assist. Interv. Int. Conf. Med. Image Comput. Comput. Assist Interv. 10, 617–625 (2007)
Nikiforidis, G.: An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine. Medical Informatics and the Internet in Medicine 30, 179–193 (2005)
Fuchs, T.J., Lange, T., Wild, P.J., Moch, H., Buhmann, J.M.: Weakly supervised cell nuclei detection and segmentation on tissue microarrays of renal cell carcinoma. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 173–182. Springer, Heidelberg (2008)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Secaucus (2003)
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Fuchs, T.J. et al. (2009). Randomized Tree Ensembles for Object Detection in Computational Pathology. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_35
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DOI: https://doi.org/10.1007/978-3-642-10331-5_35
Publisher Name: Springer, Berlin, Heidelberg
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