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arXiv Computer Vision

Recursive Uncertainty-Gated Image Registration for Learning-based Algorithms

The paper introduces Recursive Uncertainty-Gated Image Registration (RUGI), an iterative refinement method that updates deformation fields predicted by learning-based registration models using a gating map. Two gating strategies are explored: an uncertainty-based approach and an image residual error approach, both concentrating updates on difficult regions. Experiments on cardiac MRI and echocardiography datasets show that RUGI consistently improves registration accuracy, with the error-gated variant reducing MSE by 27‑37% on pretrained models and lowering ejection fraction estimation errors.

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arXiv Computer Vision
Sep 18

UniReg: Conditional Unified Model for Medical Image Registration

UniReg is a conditional unified model for medical image registration that adapts deformation field estimation based on anatomical priors, registration type constraints, and instance-specific features. It combines the precision of task‑specific learning with the generalization of traditional optimization, enabling effective alignment across diverse CT and MR scenarios within a single framework. Experiments show UniReg outperforms state‑of‑the‑art learning‑based methods in accuracy while providing strong cross‑scenario generalization and reducing training cost and model redundancy.

By Zi Li, Jianpeng Zhang, Tai Ma, Tony C. W. Mok, Yan-Jie Zhou, Zeli Chen, Xianghua Ye, Le Lu, Cheng Chen, Dakai Jin
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Hugging Face Trending Papers
Aug 3

An Accessible Solution for Deformable Image Registration Compared with Learning-Based Approaches

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.

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arXiv AI
Sep 16

MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis

arXiv:2609.17169v1 Announce Type: cross Abstract: Forecasting anatomical changes such as tumor growth and neurodegeneration is a challenging generative vision task. Morphological evolution is subtle...

By Anna Oliveras, Roger Mar\'i, Rafael Redondo, Oriol Guardi\`a, Cynthia Ifeyinwa Ugwu, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva
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arXiv Computer Vision
Sep 25

When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation

The paper examines how residual misalignments from registration procedures introduce structured label noise in supervised synthetic CT (sCT) generation. It shows that voxel‑wise metrics are heavily influenced by the consistency between training and evaluation registrations, and that training with anatomically consistent registrations reduces variability and improves robustness. Introducing a perceptual loss based on a pretrained Segment Anything encoder yields sharper, more anatomically coherent sCT and highlights the need for anatomy‑oriented evaluation.

By Valentin Boussot, Cedric Hemon, Caroline Lafond, Jean-Claude Nunes, Jean-Louis Dillenseger
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arXiv AI
Aug 25

SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging

The paper introduces Segment Anything Small (SAS), a data‑augmentation method that improves deep‑learning segmentation of small anatomical structures in ultrasound images. SAS uses two transformations: resizing and embedding organ thumbnails into a black background to vary organ scale, and adding noise to regions of interest to mimic tissue texture variability. Experiments on one internal and five external datasets show Dice score gains up to 0.35, with an average improvement of 0.16, and demonstrate that SAS enhances model robustness and generalizability without adding hallucinations or artifacts.

By Danielle L. Ferreira, Ahana Gangopadhyay, Hsi-Ming Chang, Ravi Soni, Gopal Avinash
ragcomputer-visionfine-tuningsafety
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arXiv Computer Vision
Sep 1

The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification

arXiv:2608.29246v1 Announce Type: cross Abstract: Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Now...

By Olivier Bernard, William A. Romero R., Cyprien Bouton, Celia Goujat, Hang Jung Ling, Pierre-Marc Jodoin, Fumin Guo, Calder Sheagren, Graham Wright, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Hairui Wang, Xiaomei Wu, Franz Thaler, Gernot Plank, Martin Urschler, Ricardo M. Rosales, Esther Pueyo, Nicolas Duchateau, Frederic Cervenansky, Patrick Clarysse, Loic Belle, Thomas Bochaton, Nathan Mewton, Magalie Viallon, Pierre Croisille
computer-visionfine-tuningbenchmarks
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