Abstract
In brain image analysis many of the current pipelines are not robust to the presence of lesions which degrades their accuracy and robustness. For example, performance of classic medical image processing operations such as non-linear registration or segmentation rapidly decreases when dealing with lesions. To minimize their impact, some authors have proposed to inpaint these lesions so classic pipelines can be used. However, this requires to manually delineate the regions of interest which is time consuming. In this paper, we propose a deep network that is able to blindly inpaint lesions in brain images automatically allowing current pipelines to robustly operate under pathological conditions. We demonstrate the improved robustness/accuracy in the brain segmentation problem using the SPM12 pipeline with our automatically inpainted images.
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Acknowledgement
This research was supported by the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain. This work also benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10- IDEX- 03- 02, HL-MRI Project), Cluster of excellence CPU and the CNRS. The authors gratefully acknowledge the support of NVIDIA Corporation with their donation of the TITAN X GPU used in this research.
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Manjón, J.V. et al. (2020). Blind MRI Brain Lesion Inpainting Using Deep Learning. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2020. Lecture Notes in Computer Science(), vol 12417. Springer, Cham. https://doi.org/10.1007/978-3-030-59520-3_5
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DOI: https://doi.org/10.1007/978-3-030-59520-3_5
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