解析方法在医学图像配准中胜过深度学习，实现高精度与可解释性。
An Accessible Solution for Deformable Image Registration Compared with Learning-Based Approaches
- 基于参数化总变差的解析方法，结合贝叶斯优化自动调参。
- 在Lung250M-4B数据集上达到当前最优性能，显著优于深度学习模型。
- 代码开源，适合需要可解释性的医疗影像研究者使用。
可变形图像配准（DIR）是医学图像分析的核心问题；然而，与分类、分割等标注决策问题不同，配准需满足严格的物理约束。尽管深度学习方法提升了配准速度，但其模型可解释性远低于具有显式目标和物理意义的手工设计方法。本文研究基于参数化总变差的pTVreg方法，在不同实现下表现各异，提出一种易于使用的实现方式，并引入贝叶斯优化框架，仅需少量样本即可自动为任意DIR任务设定自参数。在Lung250M-4B基准测试中，该方法取得当前最优结果，显著优于现有深度学习方案及其他pTVreg变体。源代码将公开于https://github.com/oazeybekoglu/ptvreg-python。
原文摘要 · Abstract (English)
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 .
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