A Comparison of Open Source Deep Learning Based Models for Automatic Tumor Segmentation in Radiotherapy Planning of Glioblastoma Multiforme Adult Patients

Glioblastoma multiforme (GBM) is a high-grade glioma with poor overall survival and high local recurrence rates. Standard treatment includes surgical removal of the tumor followed by radiotherapy (RT) and adjuvant chemotherapy. In RT planning, general target volume is manually defined using contrast-enhanced T1-weighted (T1-ce) and T2-FLAIR MR images to locate the residual enhancing tumor region and extend contours on visible perifocal edema, respectively. However, planning process requires strong knowledge and long times to clearly define tumor contours. This study aims to find the best candidate between several open-source segmentation networks to build an automatic tumor segmentation tool for the enhancing tumor delineation in RT planning using T1-ce MR sequences. For that purpose, four deep learning models including nnU-Net, TransUNet, SwinUNETR and MedNeXt, were trained and tested under different hyperparameter's conditions using BraTS public dataset and an additional private cohort of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 3}$</tex> patients as external test. nuU-Net and MedNeXt showed best performances reaching same Dice Score (DSC) in both validation (0.89) and external test (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 8 1}$</tex>) sets. A peritumoral edema segmentation model on T2FLAIR images built on nnU-Net's best configuration was also implemented with success. Index Terms-glioblastoma multiforme, radiotherapy, tumor segmentation, deep learning, U-Net.