Abstract
This paper proposes a class-specific regression random forest as a fully automatic algorithm for extraction of the standard view planes from 3D echocardiography. We present a natural, continuous parameterization of the plane detection task and address it by the regression voting algorithm. We integrate the voxel class label information into the training of the regression forest to exclude irrelevant classes from voting. This yields a class-specific regression forest. Two objective functions are employed to optimize for both the class label and the class-conditional regression parameters. During testing, high uncertainty class-specific predictors are excluded from voting, maximizing the confidence of the continuous output predictions.
The method is validated on a dataset of 25 3D echocardiographic images. Compared to the classic regression forest [1], the class-specific regression forest demonstrates a significant improvement in the accuracy of the detected planes.
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References
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Criminisi, A., Shotton, J., Robinson, 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 Workshop MCV. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2010)
Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A.: Neighbourhood approximation forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention. LNCS, vol. 7512, pp. 75–82. Springer, Heidelberg (2012)
Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic location of vertebrae on DXA images using random forest regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention. LNCS, vol. 7512, pp. 361–368. Springer, Heidelberg (2012)
Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Moller, A.M., Nekolla, S., Navab, N.: Fast multiple organs detection and localization in whole-body MR dixon sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)
Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A.: Joint classification-regression forests for spatially structured multi-object segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision – ECCV 2012, vol. 7575, pp. 870–881. Springer, Heidelberg (2012)
Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.: Hough forests for object detection, tracking, and action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2188–2202 (2011)
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, vol. 6361, pp. 111–118. Springer, Heidelberg (2010)
Yaqub, M., Javaid, K., Cooper, C., Noble, A.: Efficient volumetric segmentation using 3D fast-weighted random forests. In: MICCAI Workshop on Machine Learning in Medical Imaging, Toronto, Canada (2011)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: Computer Vision and Pattern Recognition, pp. 1–8. Anchorage, AK, USA (2008)
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This research was funded by the UK EPSRC on grant EP/G030693/1
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Chykeyuk, K., Yaqub, M., Alison Noble, J. (2014). Class-Specific Regression Random Forest for Accurate Extraction of Standard Planes from 3D Echocardiography. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_6
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DOI: https://doi.org/10.1007/978-3-319-05530-5_6
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