ArticleTechnology in cancer research & treatment2018
Automatic Intracranial Segmentation: Is the Clinician Still Needed?
Article in Technology in cancer research & treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.BioMed research international · 2024Article
- Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution.Physics and imaging in radiation oncology · 2023Article
- Evaluation of Atlas-based auto-segmentation of liver in MR images for Yttrium-90 selective internal radiation therapy.Journal of applied clinical medical physics · 2023Article
- Comparison of Eclipse Smart Segmentation and MIM Atlas Segment for liver delineation for yttrium-90 selective internal radiation therapy.Journal of applied clinical medical physics · 2022Article
- Reduction of inter-observer differences in the delineation of the target in spinal metastases SBRT using an automatic contouring dedicated system.Radiation oncology (London, England) · 2021Article
- Improved dosimetric accuracy with semi-automatic contour propagation of organs-at-risk in glioblastoma patients undergoing chemoradiation.Journal of applied clinical medical physics · 2019Article
- Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy.Technology in cancer research & treatmentArticle
- Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionStereotactic hypofractionated radiotherapy is an effective treatment for brain metastases in oligometastatic patients. Its planning is however time-consuming because of the number of organs at risk to be manually segmented. This study evaluates 2 automated segmentation commercial software.
methodsPatients were scanned in the treatment position. The computed tomography scan was registered on a magnetic resonance imaging and volumes were manually segmented by a clinician. Then 2 automated segmentations were performed (with iPlan and Smart Segmentation). RT STRUCT files were compared with Aquilab's Artistruct segment comparison module. We selected common segmented volume ratio as the main judging criterion. Secondary criteria were Dice-Sørensen coefficients, overlap ratio, and additional segmented volume.
resultsTwenty consecutive patients were included. Agreement between manual and automated contouring was poor. Common segmented volumes ranged from 7.71% to 82.54%, Dice-Sørensen coefficient ranged from 0.0745 to 0.8398, overlap ratio ranged from 0.0414 to 0.7275, and additional segmented volume ranged from 9.80% to 92.25%. Each software outperformed the other on some organs while performing worse on others.
conclusionNo software seemed clearly better than the other. Common segmented volumes were much too low for routine use in stereotactic hypofractionated brain radiotherapy. Manual editing is still needed.
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