Brain-Shift: Unsupervised Pseudo-Healthy Brain Synthesis for Novel Biomarker Extraction in Chronic Subdural Hematoma (2403.19415v1)
Abstract: Chronic subdural hematoma (cSDH) is a common neurological condition characterized by the accumulation of blood between the brain and the dura mater. This accumulation of blood can exert pressure on the brain, potentially leading to fatal outcomes. Treatment options for cSDH are limited to invasive surgery or non-invasive management. Traditionally, the midline shift, hand-measured by experts from an ideal sagittal plane, and the hematoma volume have been the primary metrics for quantifying and analyzing cSDH. However, these approaches do not quantify the local 3D brain deformation caused by cSDH. We propose a novel method using anatomy-aware unsupervised diffeomorphic pseudo-healthy synthesis to generate brain deformation fields. The deformation fields derived from this process are utilized to extract biomarkers that quantify the shift in the brain due to cSDH. We use CT scans of 121 patients for training and validation of our method and find that our metrics allow the identification of patients who require surgery. Our results indicate that automatically obtained brain deformation fields might contain prognostic value for personalized cSDH treatment. Our implementation is available on: github.com/Barisimre/brain-morphing
- “Presenting symptoms and functional outcome of chronic subdural hematoma patients” In Acta Neurologica Scandinavica 145.1, 2022, pp. 38–46
- “Evidence based diagnosis and management of chronic subdural hematoma: A review of the literature” In Journal of Clinical Neuroscience 50 Elsevier BV, 2018, pp. 7–15
- James Feghali, Wuyang Yang and Judy Huang “Updates in Chronic Subdural Hematoma: Epidemiology, Etiology, Pathogenesis, Treatment, and Outcome” In World Neurosurgery 141 Elsevier BV, 2020, pp. 339–345
- “Dexamethasone versus Surgery for Chronic Subdural Hematoma” In New England Journal of Medicine 388.24 Massachusetts Medical Society, 2023, pp. 2230–2240
- “Radiological prognostic factors of chronic subdural hematoma recurrence: a systematic review and meta-analysis” In Neuroradiology 63.1 Springer ScienceBusiness Media LLC, 2020, pp. 27–40
- “Midline Shift in Chronic Subdural Hematoma: Interrater Reliability of Different Measuring Methods and Implications for Standardized Rating in Embolization Trials” In Clinical Neuroradiology 32.4 Springer ScienceBusiness Media LLC, pp. 931–938
- Chun-Chih Liao, Ya-Fang Chen and Furen Xiao “Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms” In International Journal of Biomedical Imaging 2018 Hindawi Limited, 2018, pp. 1–13
- “Automated assessment of midline shift in head injury patients” In Clinical Neurology and Neurosurgery 112.9 Elsevier BV, 2010, pp. 785–790
- “Quantitative analysis of brain herniation from non-contrast CT images using deep learning” In Journal of Neuroscience Methods 349 Elsevier BV, pp. 109033
- “From hemorrhage to midline shift: A new method of tracing the deformed midline in traumatic brain injury ct images” In 2009 16th IEEE International Conference on Image Processing (ICIP), 2009
- “Automated detection of 3D midline shift in spontaneous supratentorial intracerebral haemorrhage with non-contrast computed tomography using deep convolutional neural networks” In American Journal of Translational Research 13.10 e-Century Publishing Corporation, 2021, pp. 11513
- “Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks” In World Neurosurgery 148 Elsevier BV, 2021, pp. e58–e65
- John Ashburner “A fast diffeomorphic image registration algorithm” In NeuroImage 38, 2007, pp. 95–113
- “Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces” In Medical Image Analysis 57 Elsevier BV, 2019, pp. 226–236
- “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images”, 2022 arXiv:2201.01266 [eess.IV]
- “Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies” In Tomography 8.1, 2022, pp. 497–512
- “Image quality assessment: from error visibility to structural similarity” In IEEE Transactions on Image Processing 13.4, 2004, pp. 600–612
- Harold Jeffreys “The theory of probability”, Oxford Classic Texts in the Physical Sciences London, England: Oxford University Press, 1998
- “Learning Deep Embeddings with Histogram Loss” In Advances in Neural Information Processing Systems 29 Curran Associates, Inc., 2016
- Diederik P Kingma and Jimmy Ba “Adam: A method for stochastic optimization” In arXiv preprint arXiv:1412.6980, 2014
- S. Prima, S. Ourselin and N. Ayache “Computation of the mid-sagittal plane in 3-D brain images” In IEEE Transactions on Medical Imaging 21.2 Institute of ElectricalElectronics Engineers (IEEE), 2002, pp. 122–138
- “A Log-Euclidean Framework for Statistics on Diffeomorphisms” In Lecture Notes in Computer Science Springer Berlin Heidelberg, 2006, pp. 924–931
- “VoxelMorph: A Learning Framework for Deformable Medical Image Registration” In IEEE Transactions on Medical Imaging 38.8 Institute of ElectricalElectronics Engineers (IEEE), 2019, pp. 1788–1800
- “Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science” In Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016, pp. 485–492
- “PyTorch: An Imperative Style, High-Performance Deep Learning Library” In Advances in Neural Information Processing Systems 32, 2019, pp. 8024–8035
- “Treatment Standards for Chronic Subdural Hematoma: Results from a Survey in Austrian, German, and Swiss Neurosurgical Units” In World Neurosurgery 116 Elsevier BV, 2018, pp. e983–e995
- “What is the Pressure in Chronic Subdural Hematomas? A Prospective, Population-Based Study” PMID: 21635185 In Journal of Neurotrauma 29.1, 2012, pp. 137–142
- “Development of a prognostic AI-monitor for metastatic urothelial cancer patients receiving immunotherapy” In Frontiers in Oncology 11 Frontiers Media SA, 2021, pp. 637804
- “Visual feature attribution using wasserstein gans” In Proceedings of the IEEE conference on CVPR, 2018, pp. 8309–8319
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