Abstract #3282

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

Margaux Verdier^1,2, Justine Belko¹, Jeremy Deverdun¹, Nicolas Menjot de Champfleur^1,3, Thomas Troalen², Bénédicte Maréchal^4,5,6, Emmanuelle Le Bars¹, and Till Huelnhagen^4,5,6

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.

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