Deep learning to automate the labelling of head MRI datasets for computer vision applications

• Published in: European radiology 2022-01, Vol.32 (1), p.725-736
• Main Authors: Wood, David A., Kafiabadi, Sina, Al Busaidi, Aisha, Guilhem, Emily L., Lynch, Jeremy, Townend, Matthew K., Montvila, Antanas, Kiik, Martin, Siddiqui, Juveria, Gadapa, Naveen, Benger, Matthew D., Mazumder, Asif, Barker, Gareth, Ourselin, Sebastian, Cole, James H., Booth, Thomas C.
• Format: Article
• Language: English
• Subjects: Area Under Curve; Atrophy; Automation; Blood vessels; Categories; Computer vision; Datasets; Deep Learning; Diagnostic Radiology; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Labeling; Labels; Machine learning; Magnetic Resonance Imaging; Medical imaging; Medicine; Medicine & Public Health; Model accuracy; Model testing; Neuroimaging; Neuroradiology; Object recognition; Radiography; Radiologists; Radiology; Substantia alba; Training; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-08132-0

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 l