本研究旨在评估主动学习技术在训练脑部MRI胶质瘤分割模型方面的性能。本研究使用2021年RSNA-ASNR-MICCAI脑肿瘤分割（BraTS）挑战赛提供的公开可用的训练数据集，包括1251个多机构、多参数的MR图像。本模型的输入包括经增强的T1、T2和T2 FLAIR图像以及基准手动分割结果。数据被分成包含1151个案例的训练集和包含100个案例的测试集，其中测试集在整个实验中保持不变。采用NiftyNet平台训练了深度卷积神经网络分割模型。为了测试主动学习在训练分割模型中的可行性，首先使用所有1151个训练案例训练了一个参考模型，然后使用仅575个案例和100个案例训练了另外两个模型。随后，这两个附加模型在剩余的训练案例上的预测分割结果被添加到训练数据集进行进一步训练。结果表明，主动学习方法在手动分割中可以实现与基准模型相媲美的脑胶质瘤分割模型性能（参考Dice分数为0.906，主动学习Dice分数为0.868），仅需要对28.6％的数据进行手动注释。主动学习方法在模型训练中的应用可以极大地减少准备基准训练数据所需的时间和劳动。本研究将主动学习概念应用于基于深度学习的MR图像脑胶质瘤分割中，以评估其在减少模型训练所需的手动标注基准数据量方面的可行性。© 2023. 欧洲放射学协会（ESR）。

This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.The 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.It 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.The active learning approach when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.Active 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.• 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.© 2023. European Society of Radiology (ESR).