Optimal Transport Features for Morphometric Population Analysis

• URL: http://arxiv.org/abs/2208.05891v1
• Date: Thu, 11 Aug 2022 15:46:32 GMT
• Title: Optimal Transport Features for Morphometric Population Analysis
• Authors: Samuel Gerber, Marc Niethammer, Ebrahim Ebrahim, Joseph Piven, Stephen R. Dager, Martin Styner, Stephen Aylward, Andinet Enquobahrie
• Abstract summary: We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.
• Score: 12.477630681202609
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable. We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss, minimizes sensitivity to shifts due to spatial misalignment or differences in brain topology, and separates changes due to volume differences from changes due to tissue location. We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.

Related papers

• SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery [39.66047935237083]
  We use statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding.
  arXiv  Detail & Related papers  (2024-04-30T11:49:29Z)
• Bayesian longitudinal tensor response regression for modeling neuroplasticity [0.0]
  A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially-distributed voxels. The proposed method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories.
  arXiv  Detail & Related papers  (2023-09-12T18:48:18Z)
• Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection [1.6539563017720214]
  We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate physiological deformations of the prostate. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations.
  arXiv  Detail & Related papers  (2023-09-07T11:46:59Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams [0.0]
  We present a method for implanting realistic lesions in CT slices to provide a rich and controllable set of training samples. We conclude that increasing the variability of lesions synthetically in terms of size, density, shape, and position seems to improve the performance of segmentation models for liver lesion segmentation in CT slices.
  arXiv  Detail & Related papers  (2020-08-11T13:23:04Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification [6.690876060631452]
  We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath. Each image was generated in 1.84 + 0.03 seconds using a single GTX V TITAN and pytorch. Our results imply that our style-transfer augmentation technique improves histological classification performance.
  arXiv  Detail & Related papers  (2020-07-09T18:02:49Z)

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.