Limited medical image data hinders the training of deep learning (DL) models in the biomedical field. Image augmentation can reduce the data-scarcity problem by generating variations of existing images. However, currently implemented methods require coding, excluding non-programmer users from this opportunity. We therefore present ImageAugmenter, an easy-to-use and open-source module for 3D Slicer imaging computing platform. It offers a simple and intuitive interface for applying over 20 simultaneous MONAI Transforms (spatial, intensity, etc.) to medical image datasets, all without programming. ImageAugmenter makes accessible medical image augmentation, enabling a wider range of users to improve the performance of DL models in medical image analysis by increasing the number of samples available for training.
ImageAugmenter: A user-friendly 3D Slicer tool for medical image augmentation
Zaffino, Paolo;Spadea, Maria Francesca
2024-01-01
Abstract
Limited medical image data hinders the training of deep learning (DL) models in the biomedical field. Image augmentation can reduce the data-scarcity problem by generating variations of existing images. However, currently implemented methods require coding, excluding non-programmer users from this opportunity. We therefore present ImageAugmenter, an easy-to-use and open-source module for 3D Slicer imaging computing platform. It offers a simple and intuitive interface for applying over 20 simultaneous MONAI Transforms (spatial, intensity, etc.) to medical image datasets, all without programming. ImageAugmenter makes accessible medical image augmentation, enabling a wider range of users to improve the performance of DL models in medical image analysis by increasing the number of samples available for training.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
