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arXiv · 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
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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.

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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. 2024-04-17. Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models. https://arxiv.org/abs/2404.11725

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