Wood, DA; Kafiabadi, S; Al Busaidi, A; Guilhem, EL; Lynch, J; Townend, MK; Montvila, A; ... Booth, TC; + view all Wood, DA; Kafiabadi, S; Al Busaidi, A; Guilhem, EL; Lynch, J; Townend, MK; Montvila, A; Kiik, M; Siddiqui, J; Gadapa, N; Benger, MD; Mazumder, A; Barker, G; Ourselin, S; Cole, JH; Booth, TC; - view fewer (2021) Deep learning to automate the labelling of head MRI datasets for computer vision applications. European Radiology 10.1007/s00330-021-08132-0. (In press).

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Abstract

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images.

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