Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features
Fetal MRI is an invaluable diagnostic tool complementary to ultrasound thanks to its high contrast and resolution. Motion artifacts and the arbitrary orientation of the fetus are two main challenges of fetal MRI. In this paper, we propose a method based on Random Forests with steerable features to automatically localize the heart, lungs and liver in fetal MRI. During training, all MR images are mapped into a standard coordinate system that is defined by landmarks on the fetal anatomy and normalized for fetal age. Image features are then extracted in this coordinate system. During testing, features are computed for different orientations with a search space constrained by previously detected landmarks. The method was tested on healthy fetuses as well as fetuses with intrauterine growth restriction (IUGR) from 20 to 38 weeks of gestation. The detection rate was above 90% for all organs of healthy fetuses in the absence of motion artifacts. In the presence of motion, the detection rate was 83% for the heart, 78% for the lungs and 67% for the liver. Growth restriction did not decrease the performance of the heart detection but had an impact on the detection of the lungs and liver. The proposed method can be used to initialize subsequent processing steps such as segmentation or motion correction, as well as automatically orient the 3D volume based on the fetal anatomy to facilitate clinical examination.
KeywordsRandom Forest Motion Artifact Fetal Magnetic Resonance Image Fetal Organ Ground Truth Segmentation
Unable to display preview. Download preview PDF.
- 1.Anquez, J., Angelini, E., Bloch, I.: Automatic Segmentation of Head Structures on Fetal MRI. In: ISBI, pp. 109–112. IEEE (2009)Google Scholar
- 4.Damodaram, M., Story, L., Eixarch, E., Patkee, P., Patel, A., Kumar, S., Rutherford, M.: Foetal Volumetry using Magnetic Resonance Imaging in Intrauterine Growth Restriction. Early Human Development (2012)Google Scholar
- 5.Gall, J., Lempitsky, V.: Class-specific Hough Forests for Object Detection. In: CVPR, pp. 1022–1029. IEEE (2009)Google Scholar
- 6.Ison, M., Donner, R., Dittrich, E., Kasprian, G., Prayer, D., Langs, G.: Fully Automated Brain Extraction and Orientation in Raw Fetal MRI. In: Workshop on Paediatric and Perinatal Imaging, MICCAI, pp. 17–24. Springer (2012)Google Scholar
- 7.Kainz, B., Keraudren, K., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V., Rueckert, D.: Fast Fully Automatic Brain Detection in Fetal MRI using Dense Rotation Invariant Image Descriptors. In: ISBI, pp. 1230–1233. IEEE (2014)Google Scholar
- 8.Kainz, B., Malamateniou, C., Murgasova, M., Keraudren, K., Rutherford, M., Hajnal, J., Rueckert, D.: Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 284–291. Springer, Heidelberg (2014)Google Scholar
- 9.Keraudren, K., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V., Rueckert, D.: Localisation of the Brain in Fetal MRI Using Bundled SIFT Features. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 582–589. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 10.Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Mller, A., Nekolla, S., Navab, N.: Fast Multiple Organ 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)CrossRefGoogle Scholar
- 12.Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Fast Automatic Heart Chamber Segmentation from 3D CT Data using Marginal Space Learning and Steerable Features. In: ICCV, pp. 1–8. IEEE (2007)Google Scholar