Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

Fiche publication

Date publication

avril 2018

Journal

Computers in biology and medicine

Auteurs

Membres identifiés du Cancéropôle Est :
Dr CLAVIER Jean-Baptiste , Dr NOBLET Vincent

Tous les auteurs :
Charron O, Lallement A, Jarnet D, Noblet V, Clavier JB, Meyer P

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/29455079

Résumé

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI.