Tumor Delineation for Brain Radiosurgery by a ConvNet and Non-uniform Patch Generation
Deep learning methods are actively used for brain lesion segmentation. One of the most popular models is DeepMedic, which was developed for segmentation of relatively large lesions like glioma and ischemic stroke. In our work, we consider segmentation of brain tumors appropriate to stereotactic radiosurgery which limits typical lesion sizes. These differences in target volumes lead to a large number of false negatives (especially for small lesions) as well as to an increased number of false positives for DeepMedic. We propose a new patch-sampling procedure to increase network performance for small lesions. We used a 6-year dataset from a stereotactic radiosurgery center. To evaluate our approach, we conducted experiments with the three most frequent brain tumors: metastasis, meningioma, schwannoma. In addition to cross-validation, we estimated quality on a hold-out test set which was collected several years later than the train one. The experimental results show solid improvements in both cases.
KeywordsStereotactic radiosurgery Segmentation CNN MRI
The results of sections 1, 2, 4 and 5 are based on the scientific research conducted at IITP RAS and supported by the Russian Science Foundation under grant 17-11-01390.
- 1.Leksell gamma knife society: patients treated with leksell gamma knife 1968–2016. https://www.lgksociety.com/library/annual-treatment-statistics/
- 4.Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
- 5.Ghafoorian, M., et al.: Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1414–1417. IEEE (2016)Google Scholar
- 11.Zaharchuk, G., Gong, E., Wintermark, M., Rubin, D., Langlotz, C.: Deep learning in neuroradiology. Am. J. Neuroradiol. (2018)Google Scholar