From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach

• URL: http://arxiv.org/abs/2205.06862v1
• Date: Fri, 13 May 2022 19:39:08 GMT
• Title: From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach
• Authors: Jadie Adams and Shireen Elhabian
• Abstract summary: Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. In this paper, we propose a principled framework based on the variational information bottleneck theory to relax these assumptions. Our experiments demonstrate that the proposed method provides improved accuracy and better calibrated aleatoric uncertainty estimates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existing techniques for predicting shape with aleatoric (data-dependent) uncertainty utilize a principal component analysis (PCA) based shape representation computed in isolation from the model training. This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i.e., shape) space. In this paper, we propose a principled framework based on the variational information bottleneck theory to relax these assumptions while predicting probabilistic shapes of anatomy directly from images without supervised encoding of shape descriptors. Here, the latent representation is learned in the context of the learning task, resulting in a more scalable, flexible model that better captures data non-linearity. Additionally, this model is self-regularized and generalizes better given limited training data. Our experiments demonstrate that the proposed method provides improved accuracy and better calibrated aleatoric uncertainty estimates than state-of-the-art methods.

Related papers

• Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images [1.2179682412409507]
  We propose SPI-CorrNet, a unified model that predicts 3D correspondences from sparse imaging data. Experiments on the LGE MRI left atrium dataset and Abdomen CT-1K liver datasets demonstrate that our technique enhances the accuracy and robustness of sparse image-driven SSM.
  arXiv  Detail & Related papers  (2024-07-02T03:56:20Z)
• Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images [4.424170214926035]
  Correspondence-based statistical shape modeling (SSM) facilitates population-level morphometrics. Recent advancements in deep learning have streamlined this process in inference. We introduce a weakly supervised deep learning approach to predict SSM from images using point cloud supervision.
  arXiv  Detail & Related papers  (2024-05-15T20:47:59Z)
• SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images [5.507868474642766]
  We introduce SCorP, a novel framework capable of predicting surface-based correspondences directly from unsegmented images. The proposed model streamlines the training and inference phases by removing the supervision for the correspondence prediction task.
  arXiv  Detail & Related papers  (2024-04-27T17:56:58Z)
• ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images [0.8192907805418583]
  This paper introduces a novel strategy for on-the-fly data augmentation for the Image-to-SSM framework by leveraging data-dependent noise generation or texture augmentation. Our approach achieves improved accuracy by encouraging the model to focus on the underlying geometry rather than relying solely on pixel values.
  arXiv  Detail & Related papers  (2023-07-06T20:21:12Z)
• S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences [91.48841778012782]
  We propose an unsupervised method to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods. Our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations.
  arXiv  Detail & Related papers  (2023-04-15T09:39:52Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Uncertain-DeepSSM: From Images to Probabilistic Shape Models [0.0]
  DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images. DeepSSM produces an overconfident estimate of shape that cannot be blindly assumed to be accurate. We propose Uncertain-DeepSSM as a unified model that quantifies both, data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variance.
  arXiv  Detail & Related papers  (2020-07-13T17:18:21Z)

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.