发表机构
Uppsala University; Uppsala University Hospital（乌普萨拉大学; 乌普萨拉大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出pTVreg的可及实现及自动设置参数的贝叶斯优化框架，在Lung250M-4B数据集上其性能优于现有深度学习方案及其他pTVreg变体。
AI 中文摘要
可变形图像配准（DIR）是医学图像分析的核心问题；但与分类和分割等标注决策问题不同，配准是一类涉及严格物理约束的问题。尽管深度学习方法已使配准速度更快，但与具有明确目标和可解释物理意义的手工方法相比，所得模型通常难以解释。本研究表明，在常见的可变形配准任务中，解析方法仍可产生与深度学习相当甚至更优的结果。我们在此背景下研究pTVreg，这是一种基于参数总变分的配准方法。观察到其不同实现的性能存在差异，我们引入该方法的一种可及实现，以及一个贝叶斯优化框架，该框架可从一组样本示例中为任何DIR任务自动设置自身参数。在Lung250M-4B数据集上的实验表明，我们提出的实现方案在该基准测试中达到了最先进的结果，显著优于现有的深度学习解决方案和其他作为基线的pTVreg变体。源代码将在此https URL上公开提供。
英文摘要
Deformable image registration (DIR) is a core problem in medical image analysis; but, unlike labeling decision problems such as classification and segmentation, registration is a problem class that involves stringent physical constraints. Although deep learning methods have made faster registration possible, the resulting models are often difficult to interpret compared to hand-crafted methods with explicit objectives and interpretable physical meaning. In this work, we show that an analytical method can still yield competitive and superior results to deep learning in a common deformable registration task. We study pTVreg as a parametric total variation based registration in that context. Observing its different implementations to perform at various degrees, we introduce here an accessible implementation of this method, together with a Bayesian optimization framework that automatically sets self-parameters for any DIR task from a set of sample examples. Experiments on Lung250M-4B show that our proposed implementation achieves state-of-the-art results in this benchmark, substantially superior to existing deep learning solutions and other pTVreg variants as baselines. The source code will be made publicly available at https://github.com/oazeybekoglu/ptvreg-python .
