A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms

• URL: http://arxiv.org/abs/2405.07762v2
• Date: Wed, 02 Oct 2024 12:33:38 GMT
• Title: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms
• Authors: Johan Öfverstedt, Elin Lundström, Göran Bergström, Joel Kullberg, Håkan Ahlström,
• Abstract summary: We have conducted a supervoxel-wise association study between both volumetric and tissue density features in coronary computed tomography angiograms. We developed a novel method based on image segmentation, inter-subject image registration, and robust supervoxel-based correlation analysis. We evaluate the registration methodology in terms of the Dice coefficient for the heart chambers and myocardium, and the inverse consistency of the transformations.
• Score: 1.9027456538318586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of aging. In this work we have conducted a supervoxel-wise association study between both volumetric and tissue density features in coronary computed tomography angiograms and the chronological age of a subject, to understand the localized changes in morphology and tissue density with age. To enable a supervoxel-wise study of volume and tissue density, we developed a novel method based on image segmentation, inter-subject image registration, and robust supervoxel-based correlation analysis, to achieve a statistical association study between the images and age. We evaluate the registration methodology in terms of the Dice coefficient for the heart chambers and myocardium, and the inverse consistency of the transformations, showing that the method works well in most cases with high overlap and inverse consistency. In a sex-stratified study conducted on a subset of $n=1388$ images from the SCAPIS study, the supervoxel-wise analysis was able to find localized associations with age outside of the commonly segmented and analyzed sub-regions, and several substantial differences between the sexes in the association of age and volume.

Related papers

• Towards prediction of morphological heart age from computed tomography angiography [2.0413529764205838]
  Age prediction from medical images or other health-related non-imaging data is an important approach to data-driven aging research. We studied the prediction of age from computed tomography angiography (CTA) images, which provide detailed representations of the heart morphology.
  arXiv  Detail & Related papers  (2025-04-22T10:48:27Z)
• MUSTER: Longitudinal Deformable Registration by Composition of Consecutive Deformations [0.0]
  This study introduces Multi-Session Temporal Registration (MUSTER), a novel method for longitudinal analysis of changes in medical images. MUSTER improves upon conventional pairwise registration by incorporating more than two imaging sessions to recover longitudinal deformations. We show that MUSTER can effectively identify patterns of neuro-degeneration from T1-weighted images and that these changes correlate with changes in cognition.
  arXiv  Detail & Related papers  (2024-12-19T09:22:19Z)
• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query [56.54255735943497]
  Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR)
  arXiv  Detail & Related papers  (2023-05-09T16:46:33Z)
• Medical Image Analysis using Deep Relational Learning [1.8465474345655504]
  We propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. We then propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames.
  arXiv  Detail & Related papers  (2023-03-28T16:10:12Z)
• Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning [34.82366750668948]
  We make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model. Our model accurately segments brain tissues across the lifespan and outperforms existing methods.
  arXiv  Detail & Related papers  (2023-01-03T21:54:17Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• Multi-Contrast Computed Tomography Healthy Kidney Atlas [3.0066242826634415]
  A volumetric atlas framework is needed to integrate and visualize the variability across scales. There is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT. We introduce a deep learning-based volume of interest extraction method and an automated two-stage hierarchal registration pipeline to register abdominal volumes to a high-resolution CT atlas template.
  arXiv  Detail & Related papers  (2020-12-23T01:03:16Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• Local semi-supervised approach to brain tissue classification in child brain MRI [0.0]
  Most segmentation methods in child brain MRI are supervised and are based on global intensity probabilistic computation of major brain structures. In this paper, we consider classification into major tissue classes (white matter and grey matter) and the cerebrospinal fluid. We show that our method improves detection of the tissue classes by its comparison to state-of-the-art classification techniques known as Partial Volume Estimation.
  arXiv  Detail & Related papers  (2020-05-20T06:43:41Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.