bibtext: @InProceedings{ Hol_Glioblastoma_MICCAI2024,
                   author = { Holden Helland, Ragnhild and Bouget, David and Eijgelaar, Roelant S. and De Witt Hamer, Philip C. and Barkhof, Frederik and Solheim, Ole and Reinertsen, Ingerid },
                   title = { { Glioblastoma segmentation from early post-operative MRI: challenges and clinical impact } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Post-surgical evaluation and quantification of residual tumor tissue from magnetic resonance images (MRI) is a crucial step for treatment planning and follow-up in glioblastoma care. Segmentation of enhancing residual tumor tissue from early post-operative MRI is particularly challenging due to small and fragmented lesions, post-operative bleeding, and noise in the resection cavity. Although a lot of progress has been made on the adjacent task of pre-operative glioblastoma segmentation, more targeted methods are needed for addressing the specific challenges and detecting small lesions. In this study, a state-of-the-art architecture for pre-operative segmentation was used, trained on a large in-house multi-center dataset for early post-operative segmentation. Various pre-processing, data sampling techniques, and architecture variants were explored for improving the detection of small lesions. The models were evaluated on a dataset annotated by 8 novice and expert human raters, and the performance compared against the human inter-rater variability. Trained models’ performance were shown to be on par with the performance of human expert raters. As such, automatic segmentation models have the potential to be a valuable tool in a clinical setting as an accurate and time-saving alternative, compared to the current standard manual method for residual tumor measurement after surgery.
title: Glioblastoma segmentation from early post-operative MRI: challenges and clinical impact
authors: Holden Helland, Ragnhild Bouget, David Eijgelaar, Roelant S. De Witt Hamer, Philip C. Barkhof, Frederik Solheim, Ole Reinertsen, Ingerid