Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, lear...
Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, lear...
ISBN:
(纸本)9798331314385
Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. We reassess and recalibrate performance expectations from classical and DLIR methods under access to label supervision, training time, and its generalization capabilities under minor domain shifts.
Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, lear...
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The paper proposes FireANTs, the first multi-scale Adaptive Riemannian Optimization algorithm for dense diffeomorphic image matching. One of the most critical and understudied aspects of diffeomorphic image matching a...
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We present a microstructure imaging technique for estimating compartment-specific T2 and T2* simultaneously in the human brain. Microstructure imaging with diffusion MRI (dMRI) has enabled the modelling of intra-neuri...
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Multimodal medical image fusion technology provides more comprehensive and accurate image support for clinical diagnosis and treatment by integrating complementary information from different imaging modalities. Aiming...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
Multimodal medical image fusion technology provides more comprehensive and accurate image support for clinical diagnosis and treatment by integrating complementary information from different imaging modalities. Aiming at the problem that existing methods are still insufficient in detail feature extraction and inter-modal information fusion, this paper proposes a multimodal medical image fusion method combined with an adaptive attention mechanism. First, we design the Grouped Receptive Field Attentional Convolution (GRFAConv) to solve the problem of insufficient detail feature extraction capability. With the multi-head receptive field adaptive weighting strategy of grouped convolution, the range and weight of the receptive field of the convolution kernel can be adaptively adjusted according to the different demands of local and global features of the image to improve the effect of detail retention. Second, for the problem of information fusion between different modalities, we introduce an improved CBAM attention module in the feature fusion process, which adaptively selects and enhances the features in the key regions through the channel attention and spatial attention mechanisms, which greatly improves the clarity of the fused image details and the accuracy of the information expression in the key regions. Furthermore, experimental results on several medical image datasets show that the algorithm proposed in this paper can generate relatively high-quality fused images. It not only enriches the detailed features of the image, but also achieves significant advantages in several evaluation metrics.
Objectives: Radiomics has a novel value in accurately and noninvasively characterizing non-small cell lung cancer (NSCLC), but the role of peritumoral features has not been discussed in depth. This work aims to system...
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Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using mac...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.
Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address...
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Intracerebral hemorrhage (ICH) is the second most common and deadliest form of stroke. Despite medical advances, predicting treatment outcomes for ICH remains a challenge. This paper proposes a novel prognostic model ...
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