Sep 2026· Journal of imaging informatics in medicine· 0 citations· 22 references
Medicine
TL;DR
Instant3D simplifies TotalSegmentator execution and facilitates generation of reusable 3D reconstruction outputs, and complements them by providing a streamlined workflow for ROI selection, batch processing, and multiformat export.
Abstract
Automatic segmentation is increasingly used in medical imaging, but many tools require command-line operation and environment setup. TotalSegmentator provides open-source multiorgan segmentation for computed tomography (CT) and magnetic resonance imaging (MRI) data, yet its routine use may be challenging for nontechnical users. Herein, we describe the design, implementation, and functionality of Instant3D, a lightweight, open-source graphical user interface (GUI) frontend for TotalSegmentator. Instant3D was developed in Python using PyQt6. It accepts DICOM folders, NIfTI files, and NRRD files as input and allows users to select regions of interest (ROIs) through a suggestion-enabled interface. The GUI executes TotalSegmentator as the backend. Default outputs include STL meshes for 3D visualization and NIfTI segmentation images, with optional SVG masks and CSV files containing volumetric measurements. SVG masks are interoperable with SegRef3D for slice-level review and refinement of automated segmentation results. Batch processing is supported. Functionality was tested using representative CT and MRI datasets. Instant3D successfully imported supported formats, executed TotalSegmentator through the GUI, and generated expected outputs, including STL, NIfTI, SVG, and CSV files. Representative datasets demonstrated successful workflow completion and compatibility with SegRef3D. Batch processing generated organized output files for multiple input datasets, confirming workflow feasibility. Instant3D simplifies TotalSegmentator execution and facilitates generation of reusable 3D reconstruction outputs. While not a replacement for comprehensive visualization platforms, it complements them by providing a streamlined workflow for ROI selection, batch processing, and multiformat export. Instant3D may serve as a practical tool for research, education, quantitative imaging, and 3D model generation.
Interactive segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and refine their targets. Despite substantial progress by nnInteractive and VISTA3D, reliable segmentation across diverse clinical targets remains challenging, particularly f...
Ping-Qiu Gong, Shi-Yuan Su, Fandong Zhang et al.· 0 citations
IMVS is presented, a human-in-the-loop annotation framework that composes three components into a closed loop rather than a new segmentation primitive: a lightweight 2D Slice Mask Adapter fine-tuned online from user scribbles, a frozen Volume Mask Tracker (VMT) that propagates corrected masks across adjacent slices, an...
Digitally reconstructed radiographs (DRRs) are synthetic projections derived from computed tomography (CT) data and are increasingly used in 3D synthetic image generation. However, variability in preprocessing and projection frameworks limits reproducibility and comparability across studies. We present a standardized p...
Massimo Bottini, Istiak Khan, Olivier Zanier et al.· Scientific Reports· 0 citations
Manual background selection for contrast-to-noise ratio (CNR) calculations in CT image quality assessment is time-consuming, operator-dependent, and compromises reproducibility. Advanced metrics such as Noise Power Spectrum (NPS) and Task Transfer Function (TTF) traditionally require dedicated phantom acquisitions, add...
R. Carballeira, Hayley A. Cash, Marthony L. Robins et al.· 0 citations
Quantitative assessment of the crural fascia using ultrasonography may provide clinically relevant information on peripheral musculoskeletal changes in patients with chronic stroke. However, accurate segmentation of this thin structure is challenging because of speckle noise, discontinuous boundaries, and competing hyp...
Kyuseok Kim, Haneul Lee, Ji-Youn Kim· Applied Sciences· 0 citations
BACKGROUND
Although Digital Imaging and Communications in Medicine (DICOM) metadata are widely used to manage medical imaging data and support clinical workflows, their suitability as a sole basis for automatic computed tomography (CT) series labeling and characterization is limited. DICOM metadata are frequently incon...
Yu-Tong Wen, A. Quinsten, C. S. Schmidt et al.· JMIR Medical Informatics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.