An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images

Insights into Imaging

Original Article

Andrew S. Boehringer¹  Hossein Arabi¹  Amirhossein Sanaat¹  Habib Zaidi² 

[1] Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205, Geneva, Switzerland;Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205, Geneva, Switzerland;Geneva University Neurocenter, University of Geneva, CH-1211, Geneva, Switzerland;Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, Netherlands;Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark;

关键词: MRI;    Gliomas;    Segmentation;    Deep learning;    Active learning;   

DOI  :  10.1186/s13244-023-01487-6

 received in 2023-03-24, accepted in 2023-07-22,  发布年份 2023

来源: Springer

【 摘 要 】

PurposeThis study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.MethodsThe publicly available training dataset provided for the 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting of 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well as ground truth manual segmentation were used as input for the model. The data were split into a training set of 1151 cases and testing set of 100 cases, with the testing set remaining constant throughout. Deep convolutional neural network segmentation models were trained using the NiftyNet platform. To test the viability of active learning in training a segmentation model, an initial reference model was trained using all 1151 training cases followed by two additional models using only 575 cases and 100 cases. The resulting predicted segmentations of these two additional models on the remaining training cases were then addended to the training dataset for additional training.ResultsIt was demonstrated that an active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas (0.906 reference Dice score vs 0.868 active learning Dice score) while only requiring manual annotation for 28.6% of the data.ConclusionThe active learning approach when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.Critical relevance statementActive learning concepts were applied to a deep learning-assisted segmentation of brain gliomas from MR images to assess their viability in reducing the required amount of manually annotated ground truth data in model training.Key points• This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.• The active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas.• Active learning when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.Graphical Abstract

【 授权许可】

CC BY   
© European Society of Radiology (ESR) 2023

【 图 表 】

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