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.