English

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

Image and Video Processing 2024-04-19 v1 Computer Vision and Pattern Recognition
Authors: 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
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Abstract

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.

Keywords

glioblastoma segmentation deep convolutional neural networks MRI postoperative imaging

Cite

@article{arxiv.2404.11725,
  title  = {Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models},
  author = {Santiago Cepeda and Roberto Romero and Daniel Garcia-Perez and Guillermo Blasco and Luigi Tommaso Luppino and Samuel Kuttner and Ignacio Arrese and Ole Solheim and Live Eikenes and Anna Karlberg and Angel Perez-Nunez and Trinidad Escudero and Roberto Hornero and Rosario Sarabia},
  journal= {arXiv preprint arXiv:2404.11725},
  year   = {2024}
}

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