ArticleMagma (New York, N.Y.)2016
Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures.
Article in Magma (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed, 20 citations in OpenAlex.
- Learning Task-Specific Strategies for Accelerated MRI.IEEE transactions on computational imaging · 2024Article
- Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets.Journal of pathology informatics · 2023Article
- Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation.Frontiers in human neuroscience · 2021Article
- Are Magnetic Resonance Imaging Technologies Crucial to Our Understanding of Spinal Conditions?The Journal of orthopaedic and sports physical therapy · 2019Article
- Article
- Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine.PloS one · 2019Article
- Semi-automated segmentation of magnetic resonance images for thigh skeletal muscle and fat using threshold technique after spinal cord injury.Neural regeneration research · 2018Article
- Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images.Biomedical engineering online · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic or semi-automatic segmentation of tissue types or organs is well established for X-ray-based computed tomography, with its fixed grey-scale and tissue classes with well-established ranges of Hounsfield units. MRI is much more powerful with regard to soft tissue contrast and quantitative assessment of tissue properties (e.g., perfusion, diffusion, fat content), but the principle of signal generation and recording in MRI leads to inherent problems if simple threshold based segmentation procedures are applied. In this editorial in the special issue of MAGMA on tissue segmentation, a number of relevant methodical, scientific, and clinical aspects of reliable tissue segmentation using data recording by MRI are reported and discussed.
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What OpenQuestion holds
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.