Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions
- URL: http://arxiv.org/abs/2505.19518v1
- Date: Mon, 26 May 2025 05:03:01 GMT
- Title: Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions
- Authors: Nakul Poudel, Zixin Yang, Kelly Merrell, Richard Simon, Cristian A. Linte,
- Abstract summary: Image-to-physical registration enables the fusion of pre-operative information and intra-operative data.<n>We propose a patient-specific point cloud completion approach to assist with the registration process.<n>We leverage VN-OccNet to generate a complete liver surface from a partial intra-operative point cloud.
- Score: 0.5825410941577593
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Intra-operative data captured during image-guided surgery lacks sub-surface information, where key regions of interest, such as vessels and tumors, reside. Image-to-physical registration enables the fusion of pre-operative information and intra-operative data, typically represented as a point cloud. However, this registration process struggles due to partial visibility of the intra-operative point cloud. In this research, we propose a patient-specific point cloud completion approach to assist with the registration process. Specifically, we leverage VN-OccNet to generate a complete liver surface from a partial intra-operative point cloud. The network is trained in a patient-specific manner, where simulated deformations from the pre-operative model are used to train the model. First, we conduct an in-depth analysis of VN-OccNet's rotation-equivariant property and its effectiveness in recovering complete surfaces from partial intra-operative surfaces. Next, we integrate the completed intra-operative surface into the Go-ICP registration algorithm to demonstrate its utility in improving initial rigid registration outcomes. Our results highlight the promise of this patient-specific completion approach in mitigating the challenges posed by partial intra-operative visibility. The rotation equivariant and surface generation capabilities of VN-OccNet hold strong promise for developing robust registration frameworks for variations of the intra-operative point cloud.
Related papers
- PIVOTS: Aligning unseen Structures using Preoperative to Intraoperative Volume-To-Surface Registration for Liver Navigation [11.658316634846697]
PIVOTS is a neural network that takes point clouds as input for deformation prediction.<n>We train the neural network on synthetic data simulated from a biomechanical simulation pipeline.<n>Results demonstrate superior registration performance compared to baseline methods.
arXiv Detail & Related papers (2025-07-27T16:01:26Z) - Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
arXiv Detail & Related papers (2025-07-22T13:24:45Z) - Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection [50.388465935739376]
Liver registration by overlaying preoperative 3D models onto intraoperative 2D frames can assist surgeons in perceiving the spatial anatomy of the liver clearly for a higher surgical success rate.<n>Existing registration methods rely heavily on anatomical landmark-based, which encounter two major limitations.<n>We propose a landmark-free preoperative-to-intraoperative registration framework utilizing effective self-supervised learning.
arXiv Detail & Related papers (2025-04-21T14:55:57Z) - Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery with Patches-to-Partial Matching [3.6999273555552548]
In image-guided liver surgery, the initial rigid alignment between preoperative and intraoperative data is crucial.<n>We propose a patches-to-partial matching strategy as a plug-and-play module to resolve the ambiguity.<n>It has proven effective and efficient in improving registration performance for cases with limited intraoperative visibility.
arXiv Detail & Related papers (2024-12-26T18:58:29Z) - Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering.
Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively.
We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
arXiv Detail & Related papers (2024-09-18T13:40:59Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery [47.47211257890948]
We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video.<n>We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets.<n>Our results demonstrate the superiority of our approach compared to unstructured alternatives.
arXiv Detail & Related papers (2024-02-03T00:58:05Z) - Automatic registration with continuous pose updates for marker-less
surgical navigation in spine surgery [52.63271687382495]
We present an approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner.
A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models.
An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system.
arXiv Detail & Related papers (2023-08-05T16:26:41Z) - Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network.
We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module.
Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
arXiv Detail & Related papers (2022-12-01T07:32:56Z) - Intraoperative Liver Surface Completion with Graph Convolutional VAE [10.515163959186964]
We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset.
The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver.
The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape.
arXiv Detail & Related papers (2020-09-08T17:19:31Z) - Non-Rigid Volume to Surface Registration using a Data-Driven
Biomechanical Model [0.028144129864580446]
We train a convolutional neural network to perform both the search for surface correspondences and the non-rigid registration in one step.
The network is trained on physically accurate biomechanical simulations of randomly generated, deforming organ-like structures.
We show that the network translates well to real data while maintaining a high inference speed.
arXiv Detail & Related papers (2020-05-29T17:35:23Z)
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.