Driving Points Prediction For Abdominal Probabilistic Registration

• URL: http://arxiv.org/abs/2208.03232v1
• Date: Fri, 5 Aug 2022 15:28:01 GMT
• Title: Driving Points Prediction For Abdominal Probabilistic Registration
• Authors: Samuel Joutard, Reuben Dorent, Sebastien Ourselin, Tom Vercauteren, Marc Modat
• Abstract summary: Probability displacement registration models estimate displacement distribution for a subset of points. We propose in this work to learn a driving points predictor. We evaluate the impact of our contribution on two different datasets corresponding to different modalities.
• Score: 3.4093813201755627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological heterogeneity and variability of the human abdomen. Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images. These probabilistic models are informative and robust while allowing large displacements by design. As the displacement distributions are typically estimated on a subset of points (which we refer to as driving points), due to computational requirements, we propose in this work to learn a driving points predictor. Compared to previously proposed methods, the driving points predictor is optimized in an end-to-end fashion to infer driving points tailored for a specific registration pipeline. We evaluate the impact of our contribution on two different datasets corresponding to different modalities. Specifically, we compared the performances of 6 different probabilistic displacement registration models when using a driving points predictor or one of 2 other standard driving points selection methods. The proposed method improved performances in 11 out of 12 experiments.

Related papers

• CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving [6.868387710209245]
  We propose a new benChmarking paRadIgm for evaluaTing trajEctoRy predIction Approaches (CRITERIA) We show that the proposed benchmark can produce a more accurate ranking of the models and serve as a means of characterizing their behavior.
  arXiv  Detail & Related papers  (2023-10-11T18:28:15Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Towards Better Certified Segmentation via Diffusion Models [62.21617614504225]
  segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models.
  arXiv  Detail & Related papers  (2023-06-16T16:30:39Z)
• Bayesian Hierarchical Models for Counterfactual Estimation [12.159830463756341]
  We propose a probabilistic paradigm to estimate a diverse set of counterfactuals. We treat the perturbations as random variables endowed with prior distribution functions. A gradient based sampler with superior convergence characteristics efficiently computes the posterior samples.
  arXiv  Detail & Related papers  (2023-01-21T00:21:11Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• Suitability of Different Metric Choices for Concept Drift Detection [9.76294323004155]
  Many unsupervised approaches for drift detection rely on measuring the discrepancy between the sample of two time windows. Most drift detection methods can be distinguished in what metric they use, how this metric is estimated, and how the decision threshold is found. We compare different types of estimators and metrics theoretically and empirically and investigate the relevance of the single metric components.
  arXiv  Detail & Related papers  (2022-02-19T01:11:32Z)
• Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data [0.0]
  We introduce a novel algorithm to automatically identify sub-intervals of time series that are predictive of a target task. Using data collected from a driving simulator study, we identify which spatial regions along the simulated routes tend to manifest driving behaviors that are predictive of the presence of Attention Deficit Hyperactivity Disorder (ADHD) Our experimental results show both improved over prior efforts (+10% accuracy) and good alignment between the sections identified and scripted on-road scenarios.
  arXiv  Detail & Related papers  (2021-11-12T15:36:53Z)
• Personalized Trajectory Prediction via Distribution Discrimination [78.69458579657189]
  Trarimiy prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics. We present a distribution discrimination (DisDis) method to predict personalized motion patterns. Our method can be integrated with existing multi-modal predictive models as a plug-and-play module.
  arXiv  Detail & Related papers  (2021-07-29T17:42:12Z)
• Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge [82.5462771088607]
  We propose a novel model selection metric specifically designed for ITE methods under the unsupervised domain adaptation setting. In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain.
  arXiv  Detail & Related papers  (2021-02-11T21:03:14Z)
• RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features [3.530819234519772]
  In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. The results indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.
  arXiv  Detail & Related papers  (2020-08-26T16:06:24Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

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.