Meeting Banner
Abstract #3282

EVALUATION OF A CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATED SEGMENTATION OF LOW-GRADE GLIOMAS

Margaux Verdier1,2, Justine Belko1, Jeremy Deverdun1, Nicolas Menjot de Champfleur1,3, Thomas Troalen2, Bénédicte Maréchal4,5,6, Emmanuelle Le Bars1, and Till Huelnhagen4,5,6
1I2FH , Neuroradiology, CHU Montpellier, Montpellier University, France, Montpellier, France, 2Siemens Healthcare, Saint Denis, France, 3Laboratoire Charles Coulomb, University of Montpellier, France, Montpellier, France, 4Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 5LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 6Radiology Department, Lausanne University Hospital and University of Lausanne, Switzerland, Lausanne, Switzerland

Tumor growth exceeding 8mm/year is the main indication for surgical intervention in low-grade gliomas (LGG). As manual growth assessment is very time-consuming, automated segmentation is desirable. We trained a Convolutional Neural Network (CNN) to segment LGG on 277 MRI-exams (T1+T2-FLAIR) and tested its performance on 9 unknown exams. The mean Dice Similarity Coefficient for automated segmentation was 0.72. The algorithm correctly segmented low T1 and high FLAIR values but tended to underestimate heterogeneous gliomas. Results were independent of cavity or tumor volume. Automated segmentation using CNNs seems promising for clinical practice. Performance might be improved using 3D FLAIR sequences.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Abstract

Keywords