JS-RegNeXt: A ConvNeXt-Based Few-Shot JSR Framework With Correlation Awareness and Multi-Scale Prediction Consistency

Conventional label-constrained (LC) medical image registration methods are extremely dependent on the number of labels, resulting in the overfitting problem when the number of labels is insufficient. Recently, joint segmentation and registration (JSR) methods have demonstrated promising results for the LC registration tasks in few-shot situations. However, these methods typically lack global correlation awareness of the images to be registered and cannot robustly perceive global semantic information, leading to suboptimal registration performance on anatomy with low contrast or blurred boundaries. Therefore, we propose a novel JS-RegNeXt framework for few-shot label-constrained registration for medical images, which consists of segmentation and registration modules. Specifically, the segmentation module perceives global semantic information, and the registration module generates synthetic labeled data to fine-tune it. For the segmentation module, a SegNet with multi-scale prediction consistency is designed to mitigate the uncertainty introduced by synthesized data and to improve the robustness of semantic perception, even in low-contrast regions. For the registration module, a RegNeXt is proposed to achieve correlation awareness between images and leverages the large receptive field of ConvNeXt to enhance global perception. This design improves robustness in low-contrast regions, leading to more accurate and reliable image registration. Experiments on two public 3D medical image datasets, cardiac CT and brain MRI, show that our JS-RegNeXt achieves improvements in both segmentation and registration tasks compared to many state-of-the-art methods. It demonstrates that our JS-RegNeXt framework has great potential for clinical application.