Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.

Autores de IIS La Fe

• Matías Fernández Patón

  Autor

• Alejandro Montoya Filardi

  Autor

• Adrián Galiana Bordera

  Autor

• Pedro Miguel Martínez Gironés

  Autor

• Diana Veiga Canuto

  Autor

• Blanca Martínez De Las Heras

  Autor

• Leonor Cerdá Alberich

  Autor

• 

  Luis Martí Bonmatí

  Autor

Grupos

• Investigacion Biomédica en Imagen (GIBI 2-30)

• Investigación Clínica y Traslacional en Cáncer

Abstract

Diffuse midline glioma (DMG) H3 K27M-altered is a rare pediatric brainstem cancer with poor prognosis. To advance the development of predictive models to gain a deeper understanding of DMG, there is a crucial need for seamlessly integrating automatic and highly accurate tumor segmentation techniques. There is only one method that tries to solve this task in this cancer; for that reason, this study develops a modified CNN-based 3D-Unet tool to automatically segment DMG in an accurate way in magnetic resonance (MR) images. The dataset consisted of 52 DMG patients and 70 images, each with T1W and T2W or FLAIR images. Three different datasets were created: T1W images, T2W or FLAIR images, and a combined set of T1W and T2W/FLAIR images. Denoising, bias field correction, spatial resampling, and normalization were applied as preprocessing steps to the MR images. Patching techniques were also used to enlarge the dataset size. For tumor segmentation, a 3D U-Net architecture with residual blocks was used. The best results were obtained for the dataset composed of all T1W and T2W/FLAIR images, reaching an average Dice Similarity Coefficient (DSC) of 0.883 on the test dataset. These results are comparable to other brain tumor segmentation models and to state-of-the-art results in DMG segmentation using fewer sequences. Our results demonstrate the effectiveness of the proposed 3D U-Net architecture for DMG tumor segmentation. This advancement holds potential for enhancing the precision of diagnostic and predictive models in the context of this challenging pediatric cancer.

© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.

Keywords

• Deep learningx; Diffuse midline glioma; Magnetic resonance; Pediatric oncology; Segmentation