Data augmentation for multi-organ detection in medical images
Maryam Hammami, Denis P. Friboulet, Razmig Kéchichian · 2020
We propose a deep learning solution to the problem of object detection in 3D medical 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) and You Only Look Once (YOLO) combined method for data augmentation from one modality to another via CycleGAN and organ detection from generated images via YOLO. This results in a fast and accurate detection with a mean average distance of 7.95 mm for CT modality and 16.18 mm for MRI modality, 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 on CT data.