Abstract
Medical image registration is crucial for various clinical and research applications including disease diagnosis or treatment planning which require alignment of images from different modalities, time points, or subjects. Traditional registration techniques often struggle with challenges such as contrast differences, spatial distortions, and modality-specific variations. To address these limitations, we propose a method that integrates learnable edge kernels with learning-based rigid and non-rigid registration techniques. Unlike conventional layers that learn all features without specific bias, our approach begins with a predefined edge detection kernel, which is then perturbed with random noise. These kernels are learned during training to extract optimal edge features tailored to the task. This adaptive edge detection enhances the registration process by capturing diverse structural features critical in medical imaging. To provide clearer insight into the contribution of each component in our design, we introduce four variant models for rigid registration and four variant models for non-rigid registration. We evaluated our approach using a dataset provided by the Medical University across three setups: rigid registration without skull removal, with skull removal, and non-rigid registration. Additionally, we assessed performance on two publicly available datasets. Across all experiments, our method consistently outperformed state-of-the-art techniques, demonstrating its potential to improve multi-modal image alignment and anatomical structure analysis.
Abstract (translated)
医学图像配准对于包括疾病诊断或治疗计划在内的各种临床和研究应用至关重要，这些应用需要不同模态、时间点或受试者之间图像的对齐。传统配准技术在面对对比度差异、空间扭曲以及特定于模态的变化时常常遇到挑战。为了解决这些问题，我们提出了一种方法，该方法将可学习边缘核与基于学习的刚性和非刚性配准技术相结合。与传统的学习所有特征而不带有特定偏置的层不同，我们的方法从预定义的边缘检测核开始，并用随机噪声进行扰动。这些核在训练过程中被调整以提取针对任务的最佳边缘特征。这种自适应边缘检测通过捕获医学图像中关键的各种结构特征来增强配准过程。 为了更清楚地展示设计中的每个组件的作用，我们为刚性配准和非刚性配准分别引入了四种变体模型。我们使用某医科大学提供的数据集在三种配置下评估了我们的方法：无颅骨去除的刚性配准、有颅骨去除的刚性配准以及非刚性配准。此外，我们在两个公开可用的数据集上进行了性能评估。在所有实验中，我们的方法始终优于最先进的技术，证明其有能力改善多模态图像对齐和解剖结构分析。
URL
https://arxiv.org/abs/2512.01771
