Titlebook: Biomedical Image Registration; 11th International W Marc Modat,Ivor Simpson,Tony C. W. Mok Conference proceedings 2024 The Editor(s) (if ap

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書(shū)目名稱(chēng)Biomedical Image Registration影響因子(影響力)




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https://doi.org/10.1007/978-90-481-9679-1ccuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration

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https://doi.org/10.1007/978-90-481-9679-1red 3D volume of the patient. Such pre-capturing volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections can fail when the number of projections is very low as the al

時(shí)間等

Valentina Cuccio,Mario Grazianos employ multi-step network structures to break the large deformation into smaller ones and register them separately. However, these methods have two problems. First, they cannot effectively discriminate between hard-to-optimize large deformations and easy-to-optimize small deformations. This indist

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Valentina Cuccio,Mario Grazianod this approach by replacing CNNs with Attention mechanisms, claiming enhanced performance. More recently, the rise of Mamba with selective state space models has led to MambaMorph, which substituted Attention with Mamba blocks, asserting superior registration. These developments prompt a critical q

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Neural Networks, Human Time, and the Soul,cial to assess model robustness under such shifts, often accomplished using simulated domain shifts and expert annotations, e.g., landmarks. This work presents ProactiV-Reg, an annotation-free approach that utilizes a learnable image mapping: it iteratively adjusts a moving image to align with a fix

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https://doi.org/10.1057/9781403937582pends on the reliability of the used similarity metric. In this work, we systematically challenge the robustness of two such popular metrics, Mutual Information and Cross-Cumulative Residual Entropy, by employing adversarial techniques from the deep learning field. Our experiments show resistance to