{"slug": "two-parameter-flow-map-learning-for-continuous-time-diffeomorphic-image", "title": "Two-Parameter Flow Map Learning for Continuous-Time Diffeomorphic Image Registration", "summary": "A new arXiv paper (2609.10789v1) proposes a two-parameter flow map learning framework for continuous-time diffeomorphic image registration that learns the solution of a non-autonomous ODE directly, without time discretization or velocity integration during training. The authors report an average Dice improvement of 2.1% on brain MRI benchmarks, a 12% TRE reduction on lung CT, and a 2.6% Dice gain on cardiac MRI and ultrasound datasets, with consistent gains across nine datasets while preserving diffeomorphic structure. The method enforces cocycle consistency and recovers diffeomorphic mappings at inference using a small number of compositions, and can incorporate standard registration backbones.", "body_md": "arXiv:2609.10789v1 Announce Type: new \nAbstract: Diffeomorphic image registration is central to medical image analysis, enabling anatomically consistent alignment across subjects. Most learning-based diffeomorphic methods model autonomous ODEs(ordinary differential equations) by parameterizing a stationary velocity field and recovering deformations via scaling-and-squaring. While non-autonomous ODEs with time-dependent velocities increase expressiveness, existing approaches rely on numerical integration to implicitly enforce flow structure that entangles model expressiveness with discretization accuracy. We propose a framework to directly learn the continuous-time solution of a non-autonomous ODE formulated as a two-parameterflow map. By enforcing cocycle consistency, a fundamental structural property of time-varying flows, we learn the flow maps without time discretization and velocity integration during training. The framework recovers diffeomorphic mappings at inference using a small number of compositions. Our proposed framework seamlessly incorporates standard registration backbones and improves alignment accuracy consistently across nine datasets while preserving diffeomorphic structure. Notably, the proposed method achieves an average Dice improvement of 2.1% on brain MRI benchmarks, a 12% TRE reduction on lung CT, and a 2.6% Dice gain on cardiac MRI and ultrasound datasets.", "url": "https://wpnews.pro/news/two-parameter-flow-map-learning-for-continuous-time-diffeomorphic-image", "canonical_source": "https://arxiv.org/abs/2609.10789", "published_at": "2026-09-11 04:00:00+00:00", "updated_at": "2026-09-11 04:29:18.669996+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "computer-vision"], "entities": ["arXiv", "Dice", "TRE"], "alternates": {"html": "https://wpnews.pro/news/two-parameter-flow-map-learning-for-continuous-time-diffeomorphic-image", "markdown": "https://wpnews.pro/news/two-parameter-flow-map-learning-for-continuous-time-diffeomorphic-image.md", "text": "https://wpnews.pro/news/two-parameter-flow-map-learning-for-continuous-time-diffeomorphic-image.txt", "jsonld": "https://wpnews.pro/news/two-parameter-flow-map-learning-for-continuous-time-diffeomorphic-image.jsonld"}}