作者: Aggregate 時(shí)間: 2025-3-21 23:31
,Building the ‘Great March’ of Progress,earning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature on deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the作者: 一起 時(shí)間: 2025-3-22 03:43 作者: 厚顏 時(shí)間: 2025-3-22 06:09
The Dynamics of Capitalist Development,y when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered 作者: Gene408 時(shí)間: 2025-3-22 11:21
,Coming to the Forefront, 1883–1931,istration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. However, these methods cover only a limited degree of deformations. We address this limitation and define an approximate metric space for the manifold of diffeomorphis作者: 命令變成大炮 時(shí)間: 2025-3-22 15:56 作者: Hypomania 時(shí)間: 2025-3-22 20:56
https://doi.org/10.1057/9780230512320linear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variab作者: Enteropathic 時(shí)間: 2025-3-22 23:15
https://doi.org/10.1007/978-3-319-64692-3ging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by e作者: 大炮 時(shí)間: 2025-3-23 02:06
https://doi.org/10.1007/978-3-319-64692-3udes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or 作者: OVER 時(shí)間: 2025-3-23 05:56 作者: 鍍金 時(shí)間: 2025-3-23 09:41
Key Issues and Dominant Themes,ng segmentations, producing promising results across several benchmarks. In this paper, we argue that the relative failure of supervised registration approaches can in part be blamed on the use of regular U-Nets, which are jointly tasked with feature extraction, feature matching, and estimation of d作者: 緩和 時(shí)間: 2025-3-23 17:06 作者: 假裝是你 時(shí)間: 2025-3-23 19:26
Geoffrey Lee Williams,Alan Lee Williamsprises two sequential registration networks, where the local affine network can handle small deformations, and the non-rigid network is able to align texture details further. Both networks adopt the multi-magnification structure to improve registration accuracy. We train the proposed networks separa作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-3-24 01:17
Geoffrey Lee Williams,Alan Lee Williamsn. Knowledge distillation is a technique to train a faster, smaller model by learning from cues of larger models. Mobile devices with limited resources could be key in providing effective point-of-care healthcare and motivate the search of more lightweight solutions in the deep learning based image 作者: 權(quán)宜之計(jì) 時(shí)間: 2025-3-24 04:38
Distinct Structural Patterns of?the?Human Brain: A Caveat for?Registrationlinear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variability.作者: 偏見 時(shí)間: 2025-3-24 09:47 作者: Decrepit 時(shí)間: 2025-3-24 14:11
978-3-031-11202-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Evacuate 時(shí)間: 2025-3-24 17:54 作者: PHON 時(shí)間: 2025-3-24 19:02 作者: OUTRE 時(shí)間: 2025-3-25 01:14
https://doi.org/10.1057/9780230512320linear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variability.作者: osculate 時(shí)間: 2025-3-25 05:03
https://doi.org/10.1007/978-1-349-11804-5Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.作者: 小故事 時(shí)間: 2025-3-25 08:30 作者: amygdala 時(shí)間: 2025-3-25 15:01
,Building the ‘Great March’ of Progress, of pregnancy were used. We found that in the atlas several relevant brain structures were visible. In future work the atlas generation network will be incorporated and we will further explore, using the atlas, correlations between maternal periconceptional health and brain growth and development.作者: Commonplace 時(shí)間: 2025-3-25 16:37 作者: Servile 時(shí)間: 2025-3-25 23:26 作者: Mundane 時(shí)間: 2025-3-26 02:56
Geoffrey Lee Williams,Alan Lee Williamshe performance of the local affine registration algorithm. Moreover, the proposed method achieves high registration accuracy of contents at the cell level and is potentially applicable to other medical image alignment tasks.作者: 細(xì)查 時(shí)間: 2025-3-26 08:08
Towards a?4D Spatio-Temporal Atlas of?the?Embryonic and?Fetal Brain Using a?Deep Learning Approach f of pregnancy were used. We found that in the atlas several relevant brain structures were visible. In future work the atlas generation network will be incorporated and we will further explore, using the atlas, correlations between maternal periconceptional health and brain growth and development.作者: 凹室 時(shí)間: 2025-3-26 08:38 作者: Indent 時(shí)間: 2025-3-26 14:24 作者: 老人病學(xué) 時(shí)間: 2025-3-26 19:50 作者: 展覽 時(shí)間: 2025-3-26 23:38 作者: 小母馬 時(shí)間: 2025-3-27 02:38 作者: modifier 時(shí)間: 2025-3-27 06:13 作者: 爆米花 時(shí)間: 2025-3-27 12:24 作者: Ambiguous 時(shí)間: 2025-3-27 14:26 作者: 催眠 時(shí)間: 2025-3-27 19:03
0302-9743 reviewed and selected from 32 submitted papers. The papers are organized in the following topical sections: optimization, deep learning architectures, neuroimaging, diffeomorphisms, uncertainty, topology and metrics..978-3-031-11202-7978-3-031-11203-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 騷動(dòng) 時(shí)間: 2025-3-27 23:06 作者: indenture 時(shí)間: 2025-3-28 03:35
Reflections on Empirical Findings,proposed network can provide a significant performance gain while preserving the spatial smoothness in the deformation. The proposed method outperforms the state-of-the-art registration methods on two widely used publicly available datasets, indicating its effectiveness for image registration. The source code of this work is available at: ..作者: 使饑餓 時(shí)間: 2025-3-28 06:44 作者: 馬賽克 時(shí)間: 2025-3-28 13:07 作者: 果仁 時(shí)間: 2025-3-28 15:15
https://doi.org/10.1007/978-1-349-11804-5ain tumour images from the BraTS 2021 dataset. We showed that our method can better align healthy brain templates to images with brain tumours than existing state-of-the-art methods. Our PyTorch code is freely available here: ..作者: 漂亮才會(huì)豪華 時(shí)間: 2025-3-28 20:04 作者: 作繭自縛 時(shí)間: 2025-3-29 01:01 作者: Compassionate 時(shí)間: 2025-3-29 05:17 作者: 厭食癥 時(shí)間: 2025-3-29 11:00
Weighted Metamorphosis for?Registration of?Images with?Different Topologiesmetamorphic intensity additions using a time-varying spatial weight function. It can be used to model prior knowledge about the topological/appearance changes (e.g., tumour/oedema). We show that our method improves the disentanglement between anatomical (i.e., shape) and topological (i.e., appearanc作者: Aqueous-Humor 時(shí)間: 2025-3-29 12:14 作者: covert 時(shí)間: 2025-3-29 19:33 作者: 反應(yīng) 時(shí)間: 2025-3-29 21:29
DeepSTAPLE: Learning to?Predict Multimodal Registration Quality for?Unsupervised Domain Adaptationy when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered 作者: 窒息 時(shí)間: 2025-3-30 01:51 作者: 警告 時(shí)間: 2025-3-30 05:09 作者: Noisome 時(shí)間: 2025-3-30 10:15
Distinct Structural Patterns of?the?Human Brain: A Caveat for?Registrationlinear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variab作者: Junction 時(shí)間: 2025-3-30 14:39 作者: overshadow 時(shí)間: 2025-3-30 18:15
Voxelmorph++udes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or 作者: Allege 時(shí)間: 2025-3-30 23:00
Unsupervised Learning of?Diffeomorphic Image Registration via?TransMorphoposed network learns to produce and integrate time-dependent velocity fields in an LDDMM setting. The proposed method guarantees a diffeomorphic transformation and allows the transformation to be easily and accurately inverted. We also showed that, without explicitly imposing a diffeomorphism, the 作者: cumber 時(shí)間: 2025-3-31 03:50
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registrationng segmentations, producing promising results across several benchmarks. In this paper, we argue that the relative failure of supervised registration approaches can in part be blamed on the use of regular U-Nets, which are jointly tasked with feature extraction, feature matching, and estimation of d
