Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images -- Proof of Concept

• URL: http://arxiv.org/abs/2202.11001v1
• Date: Tue, 22 Feb 2022 16:07:29 GMT
• Title: Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images -- Proof of Concept
• Authors: Georgios Andreadis, Peter A.N. Bosman, Tanja Alderliesten
• Abstract summary: This work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems.
• Score: 0.7734726150561088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objective optimization approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be capable of producing a diverse set of registrations for 2D images in one run of the algorithm, representing different trade-offs between conflicting objectives in the registration problem. This allows the user to select a registration afterwards and removes the need for up-front tuning. Towards the second shortcoming, a dual-dynamic grid transformation model has proven effective at capturing large differences in 2D images. These two developments have recently been accelerated through GPU parallelization, delivering large speed-ups. Based on this accelerated version, it is now possible to extend the approach to 3D images. Concordantly, this work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes while still supporting the incorporation of annotated guidance information and multi-resolution schemes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems, forming the foundation for a new, insightful method that can include bio-mechanical properties in the registration.

Related papers

• Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers [50.576354045312115]
  Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model. We introduce a set of methods enabling cross-domain and cross-dimension transfer learning for image-to-graph transformers. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we pretrain our models on 2D satellite images before applying them to vastly different target domains in 2D and 3D.
  arXiv  Detail & Related papers  (2024-03-11T10:48:56Z)
• Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models [13.08275555017179]
  We propose ProMISe, a prompt-driven 3D medical image segmentation model using only a single point prompt. We evaluate our model on two public datasets for colon and pancreas tumor segmentations.
  arXiv  Detail & Related papers  (2023-10-30T16:49:03Z)
• MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images [0.7734726150561088]
  We present MOREA: the first evolutionary algorithm-based approach to deformable registration of 3D images capable of tackling large deformations. MOREA includes a 3D biomechanical mesh model for physical plausibility and is fully GPU-accelerated. We compare MOREA to two state-of-the-art approaches on abdominal CT scans of 4 cervical cancer patients, with the latter two approaches configured for the best results per patient.
  arXiv  Detail & Related papers  (2023-03-08T20:26:55Z)
• Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images [82.32776379815712]
  We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. We adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network. Our model is robust to the quality of the initial mesh and the error of camera pose, and can be combined with a differentiable function for test-time optimization.
  arXiv  Detail & Related papers  (2022-04-21T03:42:31Z)
• LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space [90.74976459491303]
  We introduce a prior model that is conditioned on the runtime inputs and tie this prior space to the 3D face model via a normalizing flow in the latent space. A normalizing flow bridges the two representation spaces and transforms latent samples from one domain to another, allowing us to define a latent likelihood objective. We show that our approach leads to an expressive and effective prior, capturing facial dynamics and subtle expressions better.
  arXiv  Detail & Related papers  (2022-03-15T13:22:57Z)
• DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models [31.826844124173984]
  We present a novel approach of diffusion model-based probabilistic image registration, called DiffuseMorph. Our model learns the score function of the deformation between moving and fixed images. Our method can provide flexible and accurate deformation with a capability of topology preservation.
  arXiv  Detail & Related papers  (2021-12-09T08:41:23Z)
• Deformable Image Registration using Neural ODEs [15.245085400790002]
  We present a generic, fast, and accurate diffeomorphic image registration framework that leverages neural ordinary differential equations (NODEs) Compared with traditional optimization-based methods, our framework reduces the running time from tens of minutes to tens of seconds. Our experiments show that the registration results of our method outperform state-of-the-arts under various metrics.
  arXiv  Detail & Related papers  (2021-08-07T12:54:17Z)
• Learning a Model-Driven Variational Network for Deformable Image Registration [89.9830129923847]
  VR-Net is a novel cascaded variational network for unsupervised deformable image registration. It outperforms state-of-the-art deep learning methods on registration accuracy. It maintains the fast inference speed of deep learning and the data-efficiency of variational model.
  arXiv  Detail & Related papers  (2021-05-25T21:37:37Z)
• Recurrent Multi-view Alignment Network for Unsupervised Surface Registration [79.72086524370819]
  Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. We propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. We also introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images.
  arXiv  Detail & Related papers  (2020-11-24T14:22:42Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.