SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Shape Alignment
We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LDDMM meets GANs: Generative Adversarial Networks for diffeomorphic registration
The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Genera…
Image RegistrationShape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learning
Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding bo…
DiagnosticTopology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow
Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed distance functions learned through deep neural nets. Recently DIFs-based methods have been proposed to handle shape reconstruction and dense poi…
3D geometryDecoderOrgan SegmentationTemplate MatchingUnsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recent…
CanFields: Consolidating Diffeomorphic Flows for Non-Rigid 4D Interpolation from Arbitrary-Length Sequences
We introduce Canonical Consolidation Fields (CanFields). This novel method interpolates arbitrary-length sequences of independently sampled 3D point clouds into a unified, continuous, and coherent deforming shape. Unlike…