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

arXiv:2404.11725v1 Announce Type: cross
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.

abstract arxiv automated comparison convolutional neural networks cs.cv development eess.iv images management mri networks neural networks pipeline scans segment segmentation type