[en] Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Baniasadi, Mehri ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control
Petersen, Mikkel V.; University of Aarhus
Goncalves, Jorge ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control
Horn, Andreas; Harvard University > Harvard Medical School ; Charite Medical University of Berlin
Vlasov, Vanja
Hertel, Frank ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)
Husch, Andreas ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience
External co-authors :
yes
Language :
English
Title :
DBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation
Publication date :
17 October 2022
Journal title :
Human Brain Mapping
ISSN :
1097-0193
Publisher :
John Wiley & Sons, Hoboken, United States - New York
FNR12548237 > Mehri Baniasadi > TreCoDBS > Personalised Tremor Control By Advanced Clinical Deep Brain Stimulation. > 01/11/2018 > 31/10/2022 > 2018
Funders :
FNR - Fonds National de la Recherche Lundbeckfonden Emmy Noether stipend jascha fonden DFG - Deutsche Forschungsgemeinschaft Berlin institute ion health 111 Project on computational intelligence and intelligent control National institute of health
Abelson, J. L., Curtis, G. C., Sagher, O., Albucher, R. C., Harrigan, M., Taylor, S. F., Martis, B., & Giordani, B. (2005). Deep brain stimulation for refractory obsessive-compulsive disorder. Biological Psychiatry, 57(5), 510–516.
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017). Deep learning for brain MRI segmentation: State of the art and future directions. Journal of Digital Imaging, 30, 449–459.
Aleksovski, D., Miljkovic, D., Bravi, D., & Antonini, A. (2018). Disease progression in Parkinson subtypes: The PPMI dataset. Neurological Sciences, 39(11), 1971–1976.
Anderson, D. N., Osting, B., Vorwerk, J., Dorval, A. D., & Butson, C. R. (2018). Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. Journal of Neural Engineering, 15(2), 26005.
Andersson, J. L. R., Jenkinson, M., and Smith, S. (2010). Non-linear registration, aka spatial normalization (FMRIB Technical Report TR07JA2).
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113.
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851.
Ashburner, J., & Friston, K. J. (2011). Diffeomorphic registration using geodesic shooting and gauss-Newton optimisation. NeuroImage, 55(3), 954–967.
Åstrom, M., Samuelsson, J., Roothans, J., Fytagoridis, A., Ryzhkov, M., Nijlunsing, R., & Blomstedt, P. (2018). Prediction of electrode contacts for clinically effective deep brain stimulation in essential tremor. Stereotactic and Functional Neurosurgery, 96(5), 281–288.
Bae, Y. J., Kim, J. M., Sohn, C. H., Choi, J. H., Choi, B. S., Song, Y. S., Nam, Y., Cho, S. J., Jeon, B., & Kim, J. H. (2021). Imaging the substantia nigra in Parkinson disease and other parkinsonian syndromes. Radiology, 300(2), 260–278.
Bao, S., Chung, A. C. S., Bao, S., & Chung, A. C. S. (2016). Multi-scale structured CNN with label consistency for brain MR image segmentation Multi-scale structured CNN with label consistency for brain MR image segmentation. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1163.
Basukala, D., Mukundan, R., Lim, A., Hurrell, M. A., Keenan, R. J., Dalrymple-Alford, J. C., Anderson, T. J., Myall, D. J., & Melzer, T. R. (2021). Automated segmentation of substantia nigra and red nucleus using quantitative susceptibility mapping images: Application to Parkinson's disease. Computers and Electrical Engineering, 91, 107091.
Benabid, A. L. (2003). Deep brain stimulation for Parkinson's disease. Current Opinion in Neurobiology, 13(6), 696–706.
Billot, B., Greve, D. N., Puonti, O., Thielscher, A., van Leemput, K., Fischl, B., Dalca, A. V., & Iglesias, J. E. (2021). SynthSeg: Domain randomisation for segmentation of brain scans of any contrast and resolution. arXiv.
Brebisson, A. D., & Montana, G. (2015). Deep neural networks for anatomical brain segmentation. arXiv, 20–28.
Bullitt, E., Zeng, D., Gerig, G., Aylward, S., Joshi, S., Smith, J. K., Lin, W., & Ewend, M. G. (2005). Vessel tortuosity and brain tumor malignancy: A blinded study. Academic Radiology, 12(10), 1232–1240.
Cabezas, M., Oliver, A., Lladó, X., & Freixenet, J. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3), e158–e177.
Castro, D. C., Walker, I., & Glocker, B. (2020). Causality matters in medical imaging. Nature Communications, 11(1), 3673.
Cubo, R., Fahlström, M., Jiltsova, E., Andersson, H., & Medvedev, A. (2019). Calculating deep brain stimulation amplitudes and power consumption by constrained optimization. Journal of Neural Engineering, 16(1), 16020.
Dergachyova, O., Zhao, Y., Haegelen, C., Jannin, P., & Essert, C. (2018). Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated. International Journal of Computer Assisted Radiology and Surgery, 13(7), 1117–1128.
di Martino, A., O'Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L., Beggiato, A., Bernaerts, S., Blanken, L. M., Bookheimer, S. Y., Braden, B. B., Byrge, L., Castellanos, F. X., Dapretto, M., Delorme, R., Fair, D. A., Fishman, I., … Milham, M. P. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data, 4, 1–15.
Dice, L. R. (1945). Measures of the amount of ecologic association between species: Ecological society of America stable: http://www.jstor.org/stable/1932409. Ecology, 26(3), 297–302.
Ewert, S., Plettig, P., Li, N., Chakravarty, M. M., Collins, D. L., Herrington, T. M., Kühn, A. A., & Horn, A. (2018). Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity. NeuroImage, 170, 271–282.
Feng, X., Deistung, A., Dwyer, M. G., Hagemeier, J., Polak, P., Lebenberg, J., Frouin, F., Zivadinov, R., Reichenbach, J. R., & Schweser, F. (2017). An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). Magnetic Resonance Imaging, 39, 110–122.
Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102.
González-villa, S., Oliver, A., Valverde, S., Wang, L., Zwiggelaar, R., & Lladó, X. (2016). Artificial intelligence in medicine a review on brain structures segmentation in magnetic resonance imaging. Artificial Intelligence in Medicine, 73, 45–69.
Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72.
Heckemann, R. A., Keihaninejad, S., Aljabar, P., Rueckert, D., Hajnal, J. V., & Hammers, A. (2010). Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. NeuroImage, 51(1), 221–227.
Helms, G., Draganski, B., Frackowiak, R., Ashburner, J., & Weiskopf, N. (2009). Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps. NeuroImage, 47(1), 194–198.
Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B., & Reuter, M. (2020). FastSurfer - a fast and accurate deep learning based neuroimaging pipeline. NeuroImage, 219, 117012.
Herzog, J., Volkmann, J., Krack, P., Kopper, F., Pötter, M., Lorenz, D., Steinbach, M., Klebe, S., Hamel, W., Schrader, B., Weinert, D., Müller, D., Mehdorn, H. M., & Deuschl, G. (2003). Two-year follow-up of subthalamic deep brain stimulation in Parkinson's disease. Movement Disorders, 18(11), 1332–1337.
Horn, A., & Kühn, A. A. (2015). Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations. NeuroImage, 107, 127–135.
Horn, A., Li, N., Dembeck, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., Reisert, M., Si, H., Oostenveld, R., Rorden, C., Yeh, F.-C., Fang, Q., Herrington, T. M., Vorwerk, J., & Kühn, A. A. (2019). T Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. NeuroImage, 184(3), 293–316.
Husch, A., Petersen, M. V., Gemmar, P., Goncalves, J., Sunde, N., & Hertel, F. (2018). Postoperative deep brain stimulation assessment: Automatic data integration and report generation. Brain Stimulation, 11(4), 863–866.
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211.
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.
Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143–156.
Johnson, H., Harris, G., & Williams, K. (2007). BRAINSFit: Mutual information rigid registrations of whole-brain 3D images, using the insight toolkit. Insight Journal, 57(1).
Keuken, M. C., Bazin, P. L., Schäfer, A., Neumann, J., Turner, R., & Forstmann, B. U. (2013). Ultra-high 7T MRI of structural age-related changes of the subthalamic nucleus. Journal of Neuroscience, 33(11), 4896–4900.
Kushibar, K., Valverde, S., González-villa, S., Bernal, J., Cabezas, M., Oliver, A., & Lladó, X. (2018). Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Medical Image Analysis, 48, 177–186.
LaMontagne, P. J., Benzinger, T. L. S., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., Raichle, M. E., Cruchaga, C., & Marcus, D. (2019). OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. medRxiv, 19014902.
Larson, P. S. (2014). Deep brain stimulation for movement disorders. Neurotherapeutics, 11(3), 465–474.
Lau, J. C., MacDougall, K. W., Arango, M. F., Peters, T. M., Parrent, A. G., & Khan, A. R. (2017). Ultra-high field template-assisted target selection for deep brain stimulation surgery. World Neurosurgery, 103, 531–537.
Magnotta, V. A., Matsui, J. T., Liu, D., Johnson, H. J., Long, J. D., Bolster, B. D., Mueller, B. A., Lim, K., Mori, S., Helmer, K. G., Turner, J. A., Reading, S., Lowe, M. J., Aylward, E., Flashman, L. A., Bonett, G., & Paulsen, J. S. (2012). MultiCenter reliability of diffusion tensor imaging. Brain Connectivity, 2(6), 345–355.
Malone, I. B., Cash, D., Ridgway, G. R., MacManus, D. G., Ourselin, S., Fox, N. C., & Schott, J. M. (2013). MIRIAD-public release of a multiple time point Alzheimer's MR imaging dataset. NeuroImage, 70, 33–36.
Mehta, R. and Sivaswamy, J. (2017). M-NET: A convolutional neural network for deep brain structure segmentation. 437–440.
Middlebrooks, E. H., Holanda, V. M., Tuna, I. S., Deshpande, H. D., Bredel, M., Almeida, L., Walker, H. C., Guthrie, B. L., Foote, K. D., & Okun, M. S. (2018). A method for pre-operative singlesubject thalamic segmentation based on probabilistic tractography for essential tremor deep brain stimulation. Neuroradiology, 60(3), 303–309.
Milletari, F., Ahmadi, S.-A., Kroll, C., Plate, A., Rozanski, V., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B., Bötzel, K., & Navab, N. (2017). Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding, 164, 92–102.
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005). Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimer's and Dementia, 1(1), 55–66.
Ou, Y., Sotiras, A., Paragios, N., & Davatzikos, C. (2011). DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Medical Image Analysis, 15(4), 622–639.
Pauli, W. M., Nili, A. N., & Michael Tyszka, J. (2018). Data descriptor: A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Scientific Data, 5, 1–13.
Pavese, N., Tai, Y. F., Yousif, N., Nandi, D., & Bain, P. G. (2020). Traditional trial and error versus neuroanatomic 3-dimensional image software-assisted deep brain stimulation programming in patients with Parkinson disease. World Neurosurgery, 134, e98–e102.
Pham, D. L., Xu, C., & Prince, J. L. (2000). Annu rev biomed eng. Annual Review of Biomedical Engineering, 2, 315–337.
Poldrack, R. A., Congdon, E., Triplett, W., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., & Bilder, R. M. (2016). A phenome-wide examination of neural and cognitive function. Scientific Data, 3, 1–12.
Rashed, E. A., Gomez-tames, J., & Hirata, A. (2020). End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation. Neural Networks, 125, 233–244.
Reinacher, P. C., Várkuti, B., Krüger, M. T., Piroth, T., Egger, K., Roelz, R., & Coenen, V. A. (2019). Automatic segmentation of the subthalamic nucleus: A viable option to support planning and visualization of patient-specific targeting in deep brain stimulation. Operative Neurosurgery, 17(5), 497–502.
Roy, A. G., Conjeti, S., Navab, N., & Wachinger, C. (2019). QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage, 186, 713-727.
Schönecker, T., Kupsch, A., Kühn, A. A., Schneider, G. H., & Hoffmann, K. T. (2009). Automated optimization of subcortical cerebral MR imaging-atlas coregistration for improved postoperative electrode localization in deep brain stimulation. American Journal of Neuroradiology, 30(10), 1914–1921.
Su, J. H., Thomas, F. T., Kasoff, W. S., Tourdias, T., Choi, E. Y., Rutt, B. K., & Saranathan, M. (2019). Thalamus optimized multi atlas segmentation (THOMAS): Fast, fully automated segmentation of thalamic nuclei from structural MRI. NeuroImage, 194, 272–282.
van der Lijn, F., de Bruijne, M., Klein, S., den Heijer, T., Hoogendam, Y. Y., van der Lugt, A., Breteler, M. M., & Niessen, W. J. (2012). Automated brain structure segmentation based on atlas registration and appearance models. IEEE Transactions on Medical Imaging, 31(2), 276–286.
van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … Yacoub, E. (2012). The human connectome project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231.
Varrette, S., Bouvry, P., Cartiaux, H., & Georgatos, F. (2014). Management of an academic HPC cluster: The UL experience. IEEE, 959–967.
Vogel, D., Shah, A., Coste, J., Lemaire, J. J., Wårdell, K., & Hemm, S. (2020). Anatomical brain structures normalization for deep brain stimulation in movement disorders. NeuroImage: Clinical, 27(April), 102271.
Wang, B. T., Poirier, S., Guo, T., Parrent, A. G., Peters, T. M., & Khan, A. R. (2016). Generation and evaluation of an ultra-high-field atlas with applications in DBS planning. Medical Imaging 2016: Image Processing, 9784(97840H).
Wang, J., Vachet, C., Rumple, A., Gouttard, S., Ouziel, C., Perrot, E., Du, G., Huang, X., Gerig, G., & Styner, M. (2014). Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline. Frontiers. Neuroinformatics, 8, 1–11.
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage, 31(3), 1116–1128.
