Details

Autor(en) / Beteiligte

• Gilbert, Andrew
• Marciniak, Maciej
• Rodero, Cristobal
• Lamata, Pablo
• Samset, Eigil
• Mcleod, Kristin

Titel

Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation

Ist Teil von

• IEEE transactions on medical imaging, 2021-10, Vol.40 (10), p.2783-2794

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2021

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and are transformed into realistic synthetic ultrasound images with paired labels using a CycleGAN. We demonstrate the pipeline by generating synthetic 2D echocardiography images to compare with existing deep learning ultrasound segmentation datasets. A convolutional neural network is trained to segment the left ventricle and left atrium using only synthetic images. Networks trained with synthetic images were extensively tested on four different unseen datasets of real images with median Dice scores of 91, 90, 88, and 87 for left ventricle segmentation. These results match or are better than inter-observer results measured on real ultrasound datasets and are comparable to a network trained on a separate set of real images. Results demonstrate the images produced can effectively be used in place of real data for training. The proposed pipeline opens the door for automatic generation of training data for many tasks in medical imaging as the same process can be applied to other segmentation or landmark detection tasks in any modality. The source code and anatomical models are available to other researchers.<xref ref-type="fn" rid="fn1"> 1 1 https://adgilbert.github.io/data-generation/