Paper ID: 2404.11725

Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models

Santiago Cepeda, Roberto Romero, Daniel Garcia-Perez, Guillermo Blasco, Luigi Tommaso Luppino, Samuel Kuttner, Ignacio Arrese, Ole Solheim, Live Eikenes, Anna Karlberg, Angel Perez-Nunez, Trinidad Escudero, Roberto Hornero, Rosario Sarabia

Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness.

Submitted: Apr 17, 2024