Skip to Main content Skip to Navigation
Conference papers

Blind MRI Brain Lesion Inpainting Using Deep Learning

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02966536
Contributor : Pierrick Coupé <>
Submitted on : Wednesday, October 14, 2020 - 9:49:06 AM
Last modification on : Saturday, October 17, 2020 - 3:25:40 AM

File

SASHIMI2020_008_final_v2-2.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

José Manjón, José Romero, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, et al.. Blind MRI Brain Lesion Inpainting Using Deep Learning. International Workshop on Simulation and Synthesis in Medical Imaging, Oct 2020, LIMA, Peru. pp.41-49, ⟨10.1007/978-3-030-59520-3_5⟩. ⟨hal-02966536⟩

Share

Metrics

Record views

42

Files downloads

244