Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models
<|reference_start|>Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models: 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.<|reference_end|>
@article{cepeda2024postoperative,
    title={Postoperative glioblastoma segmentation: Development of a fully
  automated pipeline using deep convolutional neural networks and comparison
  with currently available models},
    author={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},
    journal={arXiv preprint arXiv:2404.11725},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11725},
    primaryClass={eess.IV cs.CV}
}