Exploring the Impact of 3D Fast Spin Echo and Inversion Recovery Gradient Echo Sequences Magnetic Resonance Imaging Acquisition on Automated Brain Tumor Segmentation

• Published in: Mayo Clinic Proceedings. Digital health 2024-06, Vol.2 (2), p.231-240
• Main Authors: Moassefi, Mana, Faghani, Shahriar, Khanipour Roshan, Sara, Conte, Gian Marco, Rassoulinejad Mousavi, Seyed Moein, Kaufmann, Timothy J., Erickson, Bradley J.
• Format: Article
• Language: English
• ISSN: 2949-7612; 2949-7612
• DOI: 10.1016/j.mcpdig.2024.03.006

To conduct a study comparing the performance of automated segmentation techniques using 2 different contrast-enhanced T1-weighted (CET1) magnetic resonance imaging (MRI) acquisition protocol. We collected 100 preoperative glioblastoma (GBM) MRIs consisting of 50 IR-GRE and 50 3-dimensional fast spin echo (3D-FSE) image sets. Their gold-standard tumor segmentation mask was created based on the expert opinion of a neuroradiologist. Cases were randomly divided into training and test sets. We used the no new UNet (nnUNet) architecture pretrained on the 501-image public data set containing IR-GRE sequence image sets, followed by 2 training rounds with the IR-GRE and 3D-FSE images, respectively. For each patient, in the IR-GRE and 3D-FSE test sets, we had 2 prediction masks, one from the model fine-tuned with the IR-GRE training set and one with 3D-FSE. The dice similarity coefficients (DSCs) of the 2 sets of results for each case in the test sets were compared using the Wilcoxon tests. Models trained on 3D-FSE images outperformed IR-GRE models in lesion segmentation, with mean DSC differences of 0.057 and 0.022 in the respective test sets. For the 3D-FSE and IR-GRE test sets, the calculated P values comparing DSCs from 2 models were .02 and .61, respectively. Including 3D-FSE MRI in the training data set improves segmentation performance when segmenting 3D-FSE images.