CYCLE GAN-BASED DATA AUGMENTATION FOR MULTI-ORGAN DETECTION IN CT IMAGES VIA YOLO

Maryam Hammami (1, 2) , Denis Friboulet (1, 2) , Razmig Kéchichian (1, 2)

1 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
2 MYRIAD - Modeling & analysis for medical imaging and Diagnosis

We propose a deep learning solution to the problem of object detection in 3D CT images, i.e. the localization and classification of multiple structures. Supervised learning methods require large annotated datasets that are usually difficult to acquire. We thus develop a Cycle Generative Adversarial Network (CycleGAN) + You Only Look Once (YOLO) combined method for CT data augmentation using MRI source images to train a YOLO detector. This results in a fast and accurate detection with a mean average distance of 7.95 ± 6.2 mm, which is significantly better than detection without data augmentation. We show that the approach compares favorably to state-of-the-art detection methods for medical images.

Mots clés: multi-organ detection; image synthesis; data augmentation; medical imaging

Domaines: Traitement des images [eess.IV]; Machine Learning [stat.ML]; Réseau de neurones [cs.NE]

Maryam Hammami, Denis Friboulet, Razmig Kéchichian. CYCLE GAN-BASED DATA AUGMENTATION FOR MULTI-ORGAN DETECTION IN CT IMAGES VIA YOLO. 2020 IEEE International Conference on Image Processing (ICIP), Oct 2020, Abu Dhabi, United Arab Emirates. pp.390-393, ⟨10.1109/ICIP40778.2020.9191127⟩. ⟨hal-03345927⟩