Anaïs Barateau

Valentin Boussot, Cédric Hémon, Jean-Claude Nunes et al.

Image registration is fundamental in medical imaging, enabling precise alignment of anatomical structures for diagnosis, treatment planning, image-guided interventions, and longitudinal monitoring. This work introduces IMPACT (Image Metric with Pretrained model-Agnostic Comparison for Transmodality registration), a novel similarity metric designed for robust multimodal image registration. Rather than relying on raw intensities, handcrafted descriptors, or task-specific training, IMPACT defines a semantic similarity measure based on the comparison of deep features extracted from large-scale pretrained segmentation models. By leveraging representations from models such as TotalSegmentator, Segment Anything (SAM), and other foundation networks, IMPACT provides a task-agnostic, training-free solution that generalizes across imaging modalities. These features, originally trained for segmentation, offer strong spatial correspondence and semantic alignment capabilities, making them naturally suited for registration. The method integrates seamlessly into both algorithmic (Elastix) and learning-based (VoxelMorph) frameworks, leveraging the strengths of each. IMPACT was evaluated on five challenging 3D registration tasks involving thoracic CT/CBCT and pelvic MR/CT datasets. Quantitative metrics, including Target Registration Error and Dice Similarity Coefficient, demonstrated consistent improvements in anatomical alignment over baseline methods. Qualitative analyses further highlighted the robustness of the proposed metric in the presence of noise, artifacts, and modality variations. With its versatility, efficiency, and strong performance across diverse tasks, IMPACT offers a powerful solution for advancing multimodal image registration in both clinical and research settings.

Mathilde Monvoisin, Louise Piecuch, Blanche Texier et al.

The objective of this paper is to significantly reduce the manual workload required from medical professionals in complex 3D segmentation tasks that cannot be yet fully automated. For instance, in radiotherapy planning, organs at risk must be accurately identified in computed tomography (CT) or magnetic resonance imaging (MRI) scans to ensure they are spared from harmful radiation. Similarly, diagnosing age-related degenerative diseases such as sarcopenia, which involve progressive muscle volume loss and strength, is commonly based on muscular mass measurements often obtained from manual segmentation of medical volumes. To alleviate the manual-segmentation burden, this paper introduces an implicit shape prior to segment volumes from sparse slice manual annotations generalized to the multi-organ case, along with a simple framework for automatically selecting the most informative slices to guide and minimize the next interactions. The experimental validation shows the method's effectiveness on two medical use cases: assisted segmentation in the context of at risks organs for brain cancer patients, and acceleration of the creation of a new database with unseen muscle shapes for patients with sarcopenia.