ArticleThe British journal of radiology2019
Automatic multiatlas based organ at risk segmentation in mice.
Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility.Neuroradiology · 2025Article
- Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Deep learning enabled multi-organ segmentation of mouse embryos.Biology open · 2023Article
- Virtual monoenergetic micro-CT imaging in mice with artificial intelligence.Scientific reports · 2022Article
- Deep learning-based segmentation of the thorax in mouse micro-CT scans.Scientific reports · 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
- Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.Scientific reports · 2019Article
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Authors and funding
8 authors.
Funding
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Abstract
objective: During the treatment planning of a preclinical small animal irradiation, which has time limitations for reasons of animal wellbeing and workflow efficiency, the time consuming organ at risk (OAR) delineation is performed manually. This work aimed to develop, demonstrate, and quantitatively evaluate an automated contouring method for six OARs in a preclinical irritation treatment workflow.
methods: Microcone beam CT images of nine healthy mice were contoured with an in-house developed multiatlas-based image segmentation (MABIS) algorithm for six OARs: kidneys, eyes, heart, and brain. The automatic contouring was compared with the manual delineation using three quantitative metrics: the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance, and the centre of mass displacement.
results: A good agreement between manual and automatic contouring was found for OARs with sharp organ boundaries. For the brain and the heart, the median DSC was larger than 0.94, the median 95th Hausdorff Distance smaller than 0.44 mm, and the median centre of mass displacement smaller than 0.20 mm. Lower DSC values were obtained for the other OARs, but the median DSC was still larger than 0.74 for the left eye, 0.69 for the right eye, 0.89 for the left kidney and 0.80 for the right kidney.
conclusion: The MABIS algorithm was able to delineate six OARs with a relatively high accuracy. Segmenting OARs with sharp organ boundaries performed better than low contrast OARs. ADVANCES IN KNOWLEDGE:: A MABIS algorithm is developed, evaluated, and demonstrated in a preclinical small animal irradiation research workflow.
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