Intraoperative Registration by Cross-Modal Inverse Neural Rendering

• URL: http://arxiv.org/abs/2409.11983v1
• Date: Wed, 18 Sep 2024 13:40:59 GMT
• Title: Intraoperative Registration by Cross-Modal Inverse Neural Rendering
• Authors: Maximilian Fehrentz, Mohammad Farid Azampour, Reuben Dorent, Hassan Rasheed, Colin Galvin, Alexandra Golby, William M. Wells, Sarah Frisken, Nassir Navab, Nazim Haouchine,
• Abstract summary: 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.
• Score: 61.687068931599846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present in this paper 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. This disentanglement is achieved by controlling a Neural Radiance Field's appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. 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. Code and additional resources can be found at https://maxfehrentz.github.io/style-ngp/.

Related papers

• Abnormality-Driven Representation Learning for Radiology Imaging [0.8321462983924758]
  We introduce lesion-enhanced contrastive learning (LeCL), a novel approach to obtain visual representations driven by abnormalities in 2D axial slices across different locations of the CT scans. We evaluate our approach across three clinical tasks: tumor lesion location, lung disease detection, and patient staging, benchmarking against four state-of-the-art foundation models.
  arXiv  Detail & Related papers  (2024-11-25T13:53:26Z)
• Registering Neural 4D Gaussians for Endoscopic Surgery [6.270137275503944]
  We propose a novel strategy for dynamic surgical neural scene registration. By incorporating both spatial and temporal information for correspondence matching, our approach achieves superior performance. The proposed method has the potential to improve surgical planning and training, ultimately leading to better patient outcomes.
  arXiv  Detail & Related papers  (2024-07-29T17:42:45Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering [5.617649111108429]
  We present DiffPose, a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. DiffPose achieves sub-millimeter accuracy across surgical datasets at intraoperative speeds, improving upon existing unsupervised methods by an order of magnitude and even outperforming supervised baselines.
  arXiv  Detail & Related papers  (2023-12-11T13:05:54Z)
• Learning Expected Appearances for Intraoperative Registration during Neurosurgery [39.256282185354465]
  We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations.
  arXiv  Detail & Related papers  (2023-10-03T01:50:48Z)
• Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding [53.992124594124896]
  We present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.
  arXiv  Detail & Related papers  (2023-03-31T12:52:24Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis [3.5718941645696485]
  Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging. We present an un-supervised deep learning registration methodology which can accurately model affine and non-rigid trans-formations. Our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent rep-resentations.
  arXiv  Detail & Related papers  (2022-03-07T12:54:33Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.