作者: BOLT 時(shí)間: 2025-3-21 21:08 作者: MILL 時(shí)間: 2025-3-22 02:35 作者: PLAYS 時(shí)間: 2025-3-22 08:37 作者: Comedienne 時(shí)間: 2025-3-22 10:36
Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapyder or rectum, that often yield extreme deformations. In this work we analyze the impact of so-called structure guidance for learning based registration when additional segmentation information is provided to a neural network. We present a novel weakly supervised deep learning based method for multi作者: euphoria 時(shí)間: 2025-3-22 14:24
Multilevel 2D-3D Intensity-Based Image?Registrationration is the use of multilevel strategies to avoid local optima and to speed-up runtime. However, due to the different dimensionalities of the 2D fixed and 3D moving image, the setup of multilevel strategies is not straightforward..In this work, we propose an intensity-driven 2D-3D multiresolution 作者: Fierce 時(shí)間: 2025-3-22 19:25 作者: 誘拐 時(shí)間: 2025-3-23 00:45
Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance flly due to tumor resection and tissue displacement. Classical image registration techniques oftentimes fail in vicinity of the tumor, where the enclosing structures are massively altered from one scan to another. Still, locations nearby the tumor or the resection cavity are the most relevant for eva作者: VERT 時(shí)間: 2025-3-23 05:16 作者: SLAG 時(shí)間: 2025-3-23 05:59 作者: SIT 時(shí)間: 2025-3-23 12:59
Multi-channel Registration for Diffusion MRI: Longitudinal Analysis for the Neonatal Brainthus decreasing the uncertainty of deformation fields. However, the existing solutions employ only diffusion tensor imaging (DTI) derived metrics which are limited by inconsistencies in fiber-crossing regions. In this work, we extend the pipeline for registration of multi-shell high angular resoluti作者: Mitigate 時(shí)間: 2025-3-23 15:45
An Image Registration-Based Method for?EPI?Distortion Correction Based on?Opposite Phase Encoding (Cnctional MRI data (EPI) as VDMs based on actual information about the magnetic field. In this article, we compare our new image registration-based distortion correction method ‘COPE’ to an implementation of the pixelshift method. Our approach builds on existing image registration-based techniques us作者: deficiency 時(shí)間: 2025-3-23 18:45
Diffusion Tensor Driven Image Registration: A Deep Learning Approachructural or diffusion data to learn the spatial correspondences between two or more images, without taking into account the complementary information provided by using both. Here we propose a deep learning registration framework which combines the structural information provided by .-weighted (.w) i作者: Assault 時(shí)間: 2025-3-24 00:03 作者: resuscitation 時(shí)間: 2025-3-24 03:54
Conference proceedings 2020e held in Portoro?, Slovenia, in June 2020. The conference was postponed until December 2020 due to the COVID-19 pandemic...The 16 full and poster papers included in this volume were carefully reviewed and selected from 22 submitted papers. The papers are organized in the following topical sections:作者: Meager 時(shí)間: 2025-3-24 10:30
Labour Migration in Europe Volume I age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound.作者: mitten 時(shí)間: 2025-3-24 13:59
https://doi.org/10.1007/978-3-319-60309-4t trained at all) across layers and patch sizes in terms of their ability to identify hippocampal landmark points in 3D MRI data that was not included in their training. We make observations about the performance, recommend different networks and layers and make them publicly available for further evaluation.作者: labyrinth 時(shí)間: 2025-3-24 16:25
https://doi.org/10.1007/978-3-030-48705-8 cardiac cine-MRI data from 100 patients. The experimental results show that features learned from deep network are more effective than handcrafted features in guiding intra-subject registration of cardiac MR images.作者: Spongy-Bone 時(shí)間: 2025-3-24 22:00
https://doi.org/10.1007/978-3-030-48705-8mentation is based on MRtrix3 (MRtrix3: .) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.作者: 放逐 時(shí)間: 2025-3-25 01:27
Towards Segmentation and Spatial Alignment of the Human Embryonic Brain Using Deep Learning for Atla age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound.作者: SLUMP 時(shí)間: 2025-3-25 03:21 作者: 討好女人 時(shí)間: 2025-3-25 11:12
Multi-channel Image Registration of Cardiac MR Using Supervised Feature Learning with Convolutional cardiac cine-MRI data from 100 patients. The experimental results show that features learned from deep network are more effective than handcrafted features in guiding intra-subject registration of cardiac MR images.作者: 搖晃 時(shí)間: 2025-3-25 12:01
Multi-channel Registration for Diffusion MRI: Longitudinal Analysis for the Neonatal Brainmentation is based on MRtrix3 (MRtrix3: .) toolbox. The approach is quantitatively evaluated on intra-patient longitudinal registration of diffusion MRI datasets of 20 preterm neonates with 7–11 weeks gap between the scans. In addition, we present an example of an MC template generated using the proposed method.作者: induct 時(shí)間: 2025-3-25 17:44 作者: Dedication 時(shí)間: 2025-3-25 20:19 作者: SKIFF 時(shí)間: 2025-3-26 00:21
Towards Automated Spine Mobility Quantification: A Locally Rigid CT to X-ray Registration Frameworkand prone to inaccuracy. The proposed method automates this quantification by deforming a CT image in a physiologically reasonable way and matching it to the x-ray images of interest. We propose a proof of concept evaluation on synthetic data. The automatic and quantitative analysis enables reproducible results independent of the investigator.作者: 混雜人 時(shí)間: 2025-3-26 04:43 作者: Dawdle 時(shí)間: 2025-3-26 11:52 作者: neoplasm 時(shí)間: 2025-3-26 13:08 作者: Peak-Bone-Mass 時(shí)間: 2025-3-26 19:53
Labour Migration in Europe Volume IIregistration approach using the normalized gradient fields (NGF) distance measure. We discuss and empirically analyze the impact on the choice of 2D and 3D image resolutions. Furthermore, we show that our approach produces results that are comparable or superior to other state-of-the-art methods.作者: 兇殘 時(shí)間: 2025-3-26 21:32 作者: GLOOM 時(shí)間: 2025-3-27 02:51 作者: Oration 時(shí)間: 2025-3-27 06:55
The Sectoral Turn in Labour Migration Policynlinear tractogram alignment methods, where we show that our LAP-based method outperforms all others. In discussing the results, we show that a main limitation of all streamline-based nonlinear registration methods is the computational cost and that addressing such problem may lead to further improv作者: 新星 時(shí)間: 2025-3-27 12:36
https://doi.org/10.1057/9780230292536hod that works well for images with different resolutions, aspect ratios, without the necessity to perform image padding, while maintaining a low number of network parameters and fast forward pass time. The proposed method is orders of magnitude faster than the classical approach based on the iterat作者: Pruritus 時(shí)間: 2025-3-27 17:10 作者: Obverse 時(shí)間: 2025-3-27 21:17
Patrizia Battilani,Francesca Fauri structure guidance results in a comparable average Dice score of .. However, learning based registration requires only a single pass through the network, yielding computation of a deformation fields in less than 0.1?s which is more than 100 times faster than the runtime of iterative registration.作者: SLING 時(shí)間: 2025-3-28 01:07 作者: 無(wú)動(dòng)于衷 時(shí)間: 2025-3-28 02:13 作者: elucidate 時(shí)間: 2025-3-28 09:32
https://doi.org/10.1007/978-3-030-70862-7ross both modalities and species. Tissue contrast in the T2 channel is high indicating excellent tissue-boundary alignment. The DTI channel displays strong anisotropy in white matter, as well as consistent left/right orientation information even in relatively isotropic grey matter regions. Finally, 作者: 喧鬧 時(shí)間: 2025-3-28 11:13
Nonlinear Alignment of Whole Tractograms with the Linear Assignment?Problemnlinear tractogram alignment methods, where we show that our LAP-based method outperforms all others. In discussing the results, we show that a main limitation of all streamline-based nonlinear registration methods is the computational cost and that addressing such problem may lead to further improv作者: 瑪瑙 時(shí)間: 2025-3-28 15:13 作者: GULF 時(shí)間: 2025-3-28 19:30 作者: 外來(lái) 時(shí)間: 2025-3-28 23:00 作者: brachial-plexus 時(shí)間: 2025-3-29 05:44 作者: 文件夾 時(shí)間: 2025-3-29 10:59
Diffusion Tensor Driven Image Registration: A Deep Learning Approacht in terms of average Dice scores our model performs better in subcortical regions when compared to using structural data only. Moreover, average sum-of-squared differences between warped and fixed FA maps show that our proposed model performs better at aligning the diffusion data.作者: embolus 時(shí)間: 2025-3-29 11:49 作者: affinity 時(shí)間: 2025-3-29 15:37
https://doi.org/10.1007/978-3-030-50120-4artificial intelligence; bioinformatics; color image processing; computer vision; deep learning; image an作者: 2否定 時(shí)間: 2025-3-29 22:23 作者: Resection 時(shí)間: 2025-3-30 01:57 作者: 不發(fā)音 時(shí)間: 2025-3-30 05:32 作者: 冬眠 時(shí)間: 2025-3-30 08:23
