Nonfinite-modality data augmentation for brain image registration

BCSwinReg: A cross-modal attention network for CBCT-to-CT multimodal image registration

Computed tomography (CT) and cone beam computed tomography (CBCT) registration plays an important role in radiotherapy. However, the poor quality of CBCT makes CBCT-CT multimodal registration challenging. Effective feature fusion and mapping often lead to better registration results for multimodal registration. Therefore, we proposed a new backbone network BCSwinReg and a cross-modal attention module CrossSwin. Specifically, a cross-modal attention CrossSwin is designed to promote multi-modal feature fusion, map the multi-modal domain to the common domain, and thus helping the network learn the correspondence between images better. Furthermore, a new network, BCSwinReg, is proposed to discover correspondence through cross-attention exchange information, obtain multi-level semantic information through a multi-resolution strategy, and finally integrate the deformation of multi-resolutions by the divide-conquer cascade method. We performed experiments on the publicly available 4D-Lung dataset to demonstrate the effectiveness of CrossSwin and BCSwinReg. Compared with VoxelMorph, the BCSwinReg has obtained performance improvements of 3.3% in Dice Similarity Coefficient (DSC) and 0.19 in the average 95% Hausdorff distance (HD95).

Synchronous Medical Image Augmentation framework for deep learning-based image segmentation

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.