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Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13435))

Abstract

We propose a novel framework that applies atlas-based whole-brain segmentation methods to tumor-bearing MR images. Given a patient brain MR image where the tumor is initially segmented, we use a point-cloud deep learning method to predict a displacement field, which is meant to be the deformation (inverse) caused by the growth (mass-effect and cell-infiltration) of the tumor. It’s then used to warp and modify the brain atlas to represent the change so that existing atlas-based healthy-brain segmentation methods could be applied to these pathological images. To show the practicality of our method, we implement a pipeline with nnU-Net MRI tumor initial segmentation and SAMSEG, an atlas-based whole-brain segmentation method. To train and validate the deformation network, we synthesize pathological ground truth by simulating artificial tumors in healthy images with TumorSim. This method is evaluated with both real and synthesized data. These experiments show that segmentation accuracy can be improved by learning tumor-induced deformation before applying standard full brain segmentation. Our code is available at https://github.com/jiameng1010/Brain_MRI_Tumor.

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Notes

  1. 1.

    We use Scaling-and-Squaring implemented in MIRTK (https://mirtk.github.io) to transform between DISP and SVF, specifically, calculate-logarithmic-map and calculate-exponential-map.

  2. 2.

    This atlas is generated from the SAMSEG atlas comes with FreeSurfer (https://surfer.nmr.mgh.harvard.edu). We remove the non-brain labels like skull and optic-chiasm, and re-distribute the label priors because BraTS data are skull-stripped.

References

  1. Agn, M.: A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. Med. Image Anal. 54, 220–237 (2019)

    Article  Google Scholar 

  2. Atzmon, M., Maron, H., Lipman, Y.: Point convolutional neural networks by extension operators. arXiv preprint arXiv:1803.10091 (2018)

  3. Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imag. 38(8), 1788–1800 (2019)

    Article  Google Scholar 

  4. Bauer, S., Seiler, C., Bardyn, T., Buechler, P., Reyes, M.: Atlas-based segmentation of brain tumor images using a markov random field-based tumor growth model and non-rigid registration. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 4080–4083, IEEE (2010)

    Google Scholar 

  5. Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. & Biol. 58(13), R97 (2013)

    Article  Google Scholar 

  6. Cocosco, C.A., Kollokian, V., Kwan, R.K.S., Pike, G.B., Evans, A.C.: Brainweb: Online interface to a 3D MRI simulated brain database. In: NeuroImage. Citeseer (1997)

    Google Scholar 

  7. Cuadra, M.B., Pollo, C., Bardera, A., Cuisenaire, O., Villemure, J.G., Thiran, J.P.: Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans. Med. Imag. 23(10), 1301–1314 (2004)

    Article  Google Scholar 

  8. Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019)

    Article  Google Scholar 

  9. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  10. Gholami, A.: A novel domain adaptation framework for medical image segmentation. In: International MICCAI Brainlesion Workshop, pp. 289–298, Springer (2018)

    Google Scholar 

  11. Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31(10), 1941–1954 (2012)

    Article  Google Scholar 

  12. Harpold, H.L., Alvord, E.C., Jr., Swanson, K.R.: The evolution of mathematical modeling of glioma proliferation and invasion. J. Neuropathol. Exp. Neurol. 66(1), 1–9 (2007)

    Article  Google Scholar 

  13. Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Article  Google Scholar 

  14. Isensee, F., Jäger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnU-Net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop, pp. 118–132, Springer (2020)

    Google Scholar 

  15. Jia, M., Kyan, M.: Learning occupancy function from point clouds for surface reconstruction. arXiv preprint arXiv:2010.11378 (2020)

  16. Jia, M., Kyan, M.: Improving intraoperative liver registration in image-guided surgery with learning-based reconstruction. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1230–1234, IEEE (2021)

    Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  18. Klein, A., Tourville, J.: 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, 171 (2012)

    Article  Google Scholar 

  19. Kyriacou, S.K., Davatzikos, C., Zinreich, S.J., Bryan, R.N.: Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI]. IEEE Trans. Med. Imaging 18(7), 580–592 (1999)

    Article  Google Scholar 

  20. Lipková, J., et al.: Personalized radiotherapy design for glioblastoma: integrating mathematical tumor models, multimodal scans, and Bayesian inference. IEEE Trans. Med. Imaging 38(8), 1875–1884 (2019)

    Article  MathSciNet  Google Scholar 

  21. Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. In: ACM SIGGRAPH computer graphics, vol. 21, pp. 163–169, ACM (1987)

    Google Scholar 

  22. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)

    Article  Google Scholar 

  23. Mohamed, A., Zacharaki, E.I., Shen, D., Davatzikos, C.: Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Med. Image Anal. 10(5), 752–763 (2006)

    Article  Google Scholar 

  24. Pálsson, S., Cerri, S., Poulsen, H.S., Urup, T., Law, I., Van Leemput, K.: Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of mr images. arXiv preprint arXiv:2109.12334 (2021)

  25. Pollo, C., Cuadra, M.B., Cuisenaire, O., Villemure, J.G., Thiran, J.P.: Segmentation of brain structures in presence of a space-occupying lesion. Neuroimage 24(4), 990–996 (2005)

    Article  Google Scholar 

  26. Prastawa, M., Bullitt, E., Gerig, G.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13(2), 297–311 (2009)

    Article  Google Scholar 

  27. Puonti, O., Iglesias, J.E., Van Leemput, K.: Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. Neuroimage 143, 235–249 (2016)

    Article  Google Scholar 

  28. Rehman, M.U., Cho, S., Kim, J., Chong, K.T.: Brainseg-Net: Brain tumor MR image segmentation via enhanced encoder-decoder network. Diagnostics 11(2), 169 (2021)

    Article  Google Scholar 

  29. Scheufele, K., Mang, A., Gholami, A., Davatzikos, C., Biros, G., Mehl, M.: Coupling brain-tumor biophysical models and diffeomorphic image registration. Comput. Methods Appl. Mech. Eng. 347, 533–567 (2019)

    Article  MathSciNet  Google Scholar 

  30. Scheufele, K., Subramanian, S., Biros, G.: Fully automatic calibration of tumor-growth models using a single mpMRI scan. IEEE Trans. Med. Imaging 40(1), 193–204 (2020)

    Article  Google Scholar 

  31. Sederevičius, D., et al.: Reliability and sensitivity of two whole-brain segmentation approaches included in freesurfer-ASEG and SAMSEG. Neuroimage 237, 118113 (2021)

    Google Scholar 

  32. Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front. Comput. Neurosci. 13, 56 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, York University, Toronto, Canada

    Meng Jia & Matthew Kyan

Authors
  1. Meng Jia
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  2. Matthew Kyan
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Corresponding author

Correspondence to Meng Jia .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Jia, M., Kyan, M. (2022). Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_24

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  • DOI: https://doi.org/10.1007/978-3-031-16443-9_24

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  • Print ISBN: 978-3-031-16442-2

  • Online ISBN: 978-3-031-16443-9

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