ArticleMedical physics2019
Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models.Scientific reports · 2025Article
- How many models/atlases are needed as priors for capturing anatomic population variations?Medical image analysis · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeRadiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head and neck, thorax, abdomen, pelvis, and extremities. In this study, we present a new solution to trim automatically the given axial image stack into image volumes satisfying the given body region definition.
methodsThe proposed approach consists of the following steps. First, a set of reference objects is selected and roughly segmented. Virtual landmarks (VLs) for the objects are then identified by using principal component analysis and recursive subdivision of the object via the principal axes system. The VLs can be defined based on just the binary objects or objects with gray values also considered. The VLs may lie anywhere with respect to the object, inside or outside, and rarely on the object surface, and are tethered to the object. Second, a classic neural network regressor is configured to learn the geometric mapping relationship between the VLs and the boundary locations of each body region. The trained network is then used to predict the locations of the body region boundaries. In this study, we focus on three body regions - thorax, abdomen, and pelvis, and predict their superior and inferior axial locations denoted by TS(I), TI(I), AS(I), AI(I), PS(I), and PI(I), respectively, for any given volume image I. Two kinds of reference objects - the skeleton and the lungs and airways, are employed to test the localization performance of the proposed approach.
resultsOur method is tested by using low-dose unenhanced computed tomography (CT) images of 180 near whole-body
conclusionsPrecise body region identification automatically in whole-body or body region tomographic images is vital for numerous medical image analysis and analytics applications. Despite its importance, this issue has received very little attention in the literature. We present a solution to this problem in this study using the concept of virtual landmarks. The method achieves localization accuracy within 2-3 slices, which is roughly comparable to the variation found in localization by experts. As long as the reference objects can be roughly segmented, the method with its learned VLs-to-boundary location relationship and predictive ability is transferable from one image modality to another.
Indexed as
Identifiers
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.