Synchronous Medical Image Augmentation framework for deep learning-based image segmentation. (March 2023)

• Record Type: Journal Article
• Authors: Chen, Jianguo; Yang, Nan; Pan, Yuhui; Liu, Hailing; Zhang, Zhaolei
• Is Part Of: Computerized medical imaging and graphics. Volume 104(2023)
• Publication Date: 2023-03
• Subjects: Medical image augmentation -- Deep learning -- Image-label pair -- Synchronous augmentation; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102161
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Various deep learning (DL) models are widely applied in medical image analysis, and their performance depends on the scale and diversity of available training data. However, medical images often suffer from difficulty in data acquisition, imbalance in sample categories, and high cost of labeling. In addition, most image augmentation approaches mainly focus on image synthesis only for classification tasks, and rarely consider the synthetic image-label pairs for image segmentation tasks. In this paper, we focus on the medical image augmentation for DL-based image segmentation and the synchronization between augmented image samples and their labels. We design a Synchronous Medical Image Augmentation (SMIA) framework, which includes two modules based on stochastic transformation and synthesis, and provides diverse and annotated training sets for DL models. In the transform-based SMIA module, for each medical image sample and its tissue segments, a subset of SMIA factors with a random number of factors and stochastic parameter values are selected to simultaneously generate augmented samples and the paired tissue segments. In the synthesis-based SMIA module, we randomly replace the original tissues with the augmented tissues using an equivalent replacement method to synthesize new medical images, which can well maintain the original medical implications. DL-based image segmentation experiments on bone marrow smear and dermoscopic images demonstrate that the proposed SMIA framework can generate category-balanced and diverse training data, and have a positive impact on the performance of the models. Highlights: Establish an image augmentation factor library based on transformation operations. Two Synchronous Medical Image Augmentation (SMIA) algorithms for deep learning (DL). A transform-based SMIA algorithm to randomly transform tissue and segmentation images. A synthesis-based SMIA algorithm to generate new images by equivalent replacement. Provide diverse and annotated training data for segmentation and classification DL models. Graphical abstract: