Titlebook: Shape in Medical Imaging; International Worksh Christian Wachinger,Beatriz Paniagua,Jan Egger Conference proceedings 2023 The Editor(s) (if

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,Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy,e of optical colonoscopy data, characterized by highly reflective low-texture surfaces, drastic illumination changes and frequent tracking loss. Recent methods demonstrate compelling results, but suffer from: (1) frangible frame-to-frame (or frame-to-model) pose estimation resulting in many tracking

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,Anatomy-Aware Masking for Inpainting in Medical Imaging,antage of the strong priors learned by models to reconstruct the structure and texture of missing parts in images. Even though the learned features depend on the masks as well as the images, the masks used for inpainting are typically random and independent of the dataset, due to the unpredictabilit

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,Optimal Coronary Artery Segmentation Based on Transfer Learning and UNet Architecture,e considered. Nevertheless, the different segments of the coronary arteries (distal, middle and proximal) exhibit singularities, mostly linked to section changes and image visibility, that point in the direction to consider each in a singular way. In the present contribution we thoroughly analyse th

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,Unsupervised Learning of Cortical Surface Registration Using Spherical Harmonics,an effectively facilitate precise cortical surface registration. Conventional spherical registration typically involve sequential procedures for rigid and non-rigid alignments, which can potentially introduce substantial warp distortion. By contrast, the proposed method aims at joint optimization of

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,ADASSM: Adversarial Data Augmentation in Statistical Shape Models from Images,rlying population. Shape models use consistent shape representation across all the samples in a given cohort, which helps to compare shapes and identify the variations that can detect pathologies and help in formulating treatment plans. In medical imaging, computing these shape representations from

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,On the Localization of Ultrasound Image Slices Within Point Distribution Models, monitoring changes in pathological thyroid morphology. This task, however, imposes a substantial cognitive load on clinicians due to the inherent challenge of maintaining a mental 3D reconstruction of the organ. We thus present a framework for automated US image slice localization within a 3D shape