SynthesisJournal of digital imaging2018
3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review.
Synthesis in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.European radiology · 2024Article
- GPU-accelerated lung CT segmentation based on level sets and texture analysis.Scientific reports · 2024Article
- A new segment method for pulmonary artery and vein.Health information science and systems · 2023Article
- Review
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
- Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence.Nature nanotechnology · 2022Review
- Microtomographic Analysis of a Palaeolithic Wooden Point from the Ljubljanica River.Sensors (Basel, Switzerland) · 2022Article
- Quantitative assessment of renal obstruction in multi-phase CTU using automatic 3D segmentation of the renal parenchyma and renal pelvis: A proof of concept.European journal of radiology open · 2022Article
- Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography.Journal of healthcare engineering · 2021Article
- Three-dimensional segmentation of computed tomography data usingRoyal Society open science · 2020Article
- Preliminary Volumetric Calculation of the Mucosal Surface in the Nares of Lambs Using a Segmentation of Computed Tomographic Images.Frontiers in veterinary science · 2020Article
- RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.Journal of digital imaging · 2019Review
- Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization.EJNMMI research · 2019Article
- Analysis of Surface Extraction Methods Based on Gradient Operators for Computed Tomography in Metrology Applications.Materials (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.