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Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Abstract : Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score 90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score 80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.
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https://hal.inria.fr/hal-03154792
Contributor : Simona Bottani <>
Submitted on : Tuesday, April 13, 2021 - 12:04:35 PM
Last modification on : Thursday, April 15, 2021 - 3:30:14 AM

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QC_article_12_04_2021.pdf
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  • HAL Id : hal-03154792, version 2

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Simona Bottani, Ninon Burgos, Aurélien Maire, Adam Wild, Sébastian Ströer, et al.. Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. 2021. ⟨hal-03154792v2⟩

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