ARI3D: A Software for Interactive Quantification of Regions in X-Ray CT 3D Images
- URL: http://arxiv.org/abs/2508.09849v1
- Date: Wed, 13 Aug 2025 14:28:53 GMT
- Title: ARI3D: A Software for Interactive Quantification of Regions in X-Ray CT 3D Images
- Abstract summary: ARI3D aims to 1) improve phase identification; 2) Account for partial volume effect; 3) Increase the detection limit and accuracy of object quantification; and 4) Harmonize quantitative 3D analysis that can be implemented in different fields of science.
- Score: 0.3749861135832073
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: X-ray computed tomography (CT) is the main 3D technique for imaging the internal microstructures of materials. Quantitative analysis of the microstructures is usually achieved by applying a sequence of steps that are implemented to the entire 3D image. This is challenged by various imaging artifacts inherent from the technique, e.g., beam hardening and partial volume. Consequently, the analysis requires users to make a number of decisions to segment and classify the microstructures based on the voxel gray-values. In this context, a software tool, here called ARI3D, is proposed to interactively analyze regions in three-dimensional X-ray CT images, assisting users through the various steps of a protocol designed to classify and quantify objects within regions of a three-dimensional image. ARI3D aims to 1) Improve phase identification; 2) Account for partial volume effect; 3) Increase the detection limit and accuracy of object quantification; and 4) Harmonize quantitative 3D analysis that can be implemented in different fields of science.
Related papers
- Mr3D-VL: A generalist vision language foundation model for Multiparametric 3D Magnetic Resonance Imaging [3.8658612593287525]
Mr3D-VL is a visual-language foundation model for multi-parametric 3D MRI.<n>It employs an unsupervised pre-trained shared 3D encoder and 4D rotational positional embedding for dual modality-spatial integration.
arXiv Detail & Related papers (2026-08-13T01:12:34Z) - Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
arXiv Detail & Related papers (2025-09-11T23:12:55Z) - Automated Dynamic Image Analysis for Particle Size and Shape Classification in Three Dimensions [3.8300818830608345]
Current state-of-the art instruments for dynamic image analysis are largely limited to two-dimensional imaging.<n>Existing three-dimensional imaging technologies, such as computed tomography, laser scanning, and orthophotography, are limited to static objects.<n>OCULAR addresses these challenges by providing a cost-effective solution for imaging continuous particle streams using a synchronised array of optical cameras.
arXiv Detail & Related papers (2024-12-06T15:36:59Z) - TomoGRAF: A Robust and Generalizable Reconstruction Network for Single-View Computed Tomography [3.1209855614927275]
Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular data samplings.
We develop a novel TomoGRAF framework incorporating the unique X-ray transportation physics to reconstruct high-quality 3D volumes.
arXiv Detail & Related papers (2024-11-12T20:07:59Z) - Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields.
LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation.
It can generate versatile label maps by interacting with language at novel viewpoints.
arXiv Detail & Related papers (2024-10-24T17:54:42Z) - μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation [2.012378666405002]
X-ray computed microtomography (mu-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples.
extracting relevant information from 3D images requires semantic segmentation of the regions of interest.
We propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus.
arXiv Detail & Related papers (2024-06-24T15:29:08Z) - SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model [3.2554912675000818]
We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation of 3D images building on the existing Segment Anything Model.
We achieve fast and accurate segmentations in 3D images with a four-step strategy involving: user prompting with 3D polylines, volume slicing along multiple axes, slice-wide inference with a pretrained model, and recomposition and refinement in 3D.
arXiv Detail & Related papers (2024-05-10T19:26:17Z) - X-Ray: A Sequential 3D Representation For Generation [54.160173837582796]
We introduce X-Ray, a novel 3D sequential representation inspired by x-ray scans.
X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images.
arXiv Detail & Related papers (2024-04-22T16:40:11Z) - MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM.
Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking.
We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
arXiv Detail & Related papers (2024-04-01T04:57:41Z) - 3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN [4.8698443014985715]
This study introduces the 3D Residual-in-Residual Block GAN (3D RRDB-GAN) for 3D super-resolution for radiology imagery.
A key aspect of 3D RRDB-GAN is the integration of a 2.5D Dense loss function, which contributes to improved volumetric image quality and realism.
arXiv Detail & Related papers (2024-02-06T17:26:18Z) - A unified 3D framework for Organs at Risk Localization and Segmentation
for Radiation Therapy Planning [56.52933974838905]
Current medical workflow requires manual delineation of organs-at-risk (OAR)
In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation.
Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
arXiv Detail & Related papers (2022-03-01T17:08:41Z) - Cross Modality 3D Navigation Using Reinforcement Learning and Neural
Style Transfer [3.0152753984876854]
This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging.
We utilize Neural Style Transfer to create synthetic Computed Tomography (CT) agent gym environments.
Our framework does not require any labelled clinical data and integrates easily with several image translation techniques.
arXiv Detail & Related papers (2021-11-05T13:11:45Z) - 3D Reconstruction of Curvilinear Structures with Stereo Matching
DeepConvolutional Neural Networks [52.710012864395246]
We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs.
We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
arXiv Detail & Related papers (2021-10-14T23:05:47Z)
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.