Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

browbeat

Medical Image Registration Based on Uncoupled Learning and Accumulative Enhancemented with the fixed image. In order to address these issues, we present a novel medical image registration framework, namely ULAE-net, to continuously enhance the spatial transformation and establish more profound contextual dependencies under a compact network layout. Extensive experiments on 3D brai

疯狂

婚姻生活

同义联想法

impaction

SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddingsues (Elastix FFD, ANTs SyN) and learning based VoxelMorph method by at least . and . in Dice scores for two separate tasks of within-contrast-phase and across-contrast-phase registration, respectively. SAME achieves the comparable performance to the best traditional registration method, DEEDS (from

Evolve

Weakly Supervised Registration of Prostate MRI and Histopathology Imagesto align prostate boundaries and local prostate features. Although prostate segmentations were used during the training of the network, such segmentations were not needed when registering unseen images at inference time. We trained and validated our registration network with 135 and 10 patients from

Flinch

foppish

Unsupervised Diffeomorphic Surface Registration and Non-linear Modellingace data in real-world registration scenarios. The internalised deformation model is benchmarked against linear principal component analysis (PCA) achieving competitive results and improved generalisability from lower dimensions.

尽忠

Learning Dual Transformer Network for Diffeomorphic Registration model both the inter- and intra-image relevances in the embedding from both the separate and concatenated volumetric images, facilitating semantical correspondence of anatomical structures in diffeomorphic registration. Extensive quantitative and qualitative evaluations demonstrate that the DTN per

Senescent

Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases Based on Deep Learningtlases based on the entire cohort, and rapidly warp the new images to the atlas space; 2) A longitudinal constraint is employed to enforce the within-subject temporal consistency during atlas building; 3) A Correntropy based regularization loss is further exploited to enhance the robustness of our f