Keywords: Segmentation, BrainThe same deep learning model was trained for automated segmentation of glioblastoma tumour regions using either four or two MRI modalities. The performance of the model trained with only two images was found to be comparable to that of the longer protocol, suggesting that the excluded images and the consequent longer training time did not contribute significantly to the accuracy of the model. These findings strongly imply that this training approach may be beneficial for clinical applications, as it would result in reduced costs due to shorter scanner times, lower computational requirements and increased patient throughput, without compromising segmentation accuracy.
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