ArticleScientific reports2016
Multimodal Correlative Preclinical Whole Body Imaging and Segmentation.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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Who cites it
11 citing papers in PubMed.
- Advanced quantification pipeline reveals new spatial and temporal tumor characteristics in preclinical multiple myeloma.EJNMMI research · 2025Article
- Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers.Bioengineering (Basel, Switzerland) · 2024Article
- 3D whole body preclinical micro-CT database of subcutaneous tumors in mice with annotations from 3 annotators.Scientific data · 2024Article
- Optimizing SUV Analysis: A Multicenter Study on Preclinical FDG-PET/CT Highlights the Impact of Standardization.Molecular imaging and biology · 2024Observational
- Article
- Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.Theranostics · 2024Article
- Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging.Physics and imaging in radiation oncology · 2022Article
- Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans.Cancers · 2021Article
- Deep learning-enabled multi-organ segmentation in whole-body mouse scans.Nature communications · 2020Article
- A preclinical micro-computed tomography database including 3D whole body organ segmentations.Scientific data · 2018Article
- Automated quantification of bioluminescence images.Nature communications · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging.
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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.