Automatic Brain Tumor Segmentation in 2D Intra-Operative Ultrasound Images Using Magnetic Resonance Imaging Tumor Annotations

Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during the resection surgery. The lack of large annotated datasets limits the current models performances. In this paper, we investigated the use of tumor annotations in magnetic resonance imaging (MRI) scans, which are more accessible than annotations in iUS images, for training of deep learning models for iUS brain tumor segmentation. We used 180 annotated MRI scans with corresponding unannotated iUS images, and 29 annotated iUS images. Image registration was performed to transfer the MRI annotations to the corresponding iUS images before training the nnU-Net model with different configurations of the data and label origins. The results showed similar performance for a model trained with only MRI annotated tumors compared to models trained with only iUS annotations and both, and to expert annotations, indicating that MRI tumor annotations can be used as a substitute for iUS tumor annotations to train a deep learning model for automatic brain tumor segmentation in the iUS images. The best model obtained an average Dice score of 0.62 ± 0.31, compared to 0.67 ± 0.25 for an expert neurosurgeon, where the performance on larger tumors was similar, but lower for the models on smaller tumors. In addition, the results showed that removing smaller tumors from the training sets improved the results.