Data augmentation for multi-organ detection in medical images

Abstract

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.

A fully automated pipeline for the extraction of pectoralis muscle area from chest computed tomography scans

“…Therefore, although our pipeline slightly deviates from the ground truth, a fully automated approach reduces measurement variation error and therefore allows for smaller changes to be measured over time. We also acknowledge that other deep learning segmentation models such as YOLO [ 36 ] and DeepLab [ 37 ] should be explored. Future work should investigate the performance of each for this application.…”