ArticleJournal of digital imaging2021
Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 43 citations in OpenAlex.
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- A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.Journal of the Egyptian National Cancer Institute · 2026Article
- Evaluating artificial intelligence versus human-generated contours in the thorax.Physics and imaging in radiation oncology · 2026Article
- Quantifying manual editing time in autocontouring via audit log analysis.Physics and imaging in radiation oncology · 2026Article
- Auto-segmentation of organs-of-interest clinical acceptability & reproducibility framework in head and neck cancer.Physics and imaging in radiation oncology · 2026Article
- Patient-specific fine-tuning of a self-distilled transformer model for normal tissue segmentation in abdominal magnetic resonance guided adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Real-world performance evaluation of commercial autocontouring software for head and neck cancer radiotherapy.The British journal of radiology · 2025Article
- ICD lead and primary metal artifact detection and inpainting in cardiac CT images.Medical physics · 2025Article
- Geometrically focused training and evaluation of organs-at-risk segmentation via deep learning.Medical physics · 2025Article
- Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.Physical and engineering sciences in medicine · 2025Article
- Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH.Biomedical engineering online · 2024Article
- Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.Abdominal radiology (New York) · 2024Article
- Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions.Physics and imaging in radiation oncology · 2024Article
- Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study.Frontiers in oncology · 2024Article
- A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer.Physics and imaging in radiation oncology · 2023Article
- Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets.Technical innovations & patient support in radiation oncology · 2023Article
- Article
- Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer.Frontiers in oncology · 2023Article
- Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs.Frontiers in oncology · 2023Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman's rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman's rank correlation coefficients or Mann-Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ = - 0.48 versus ρ = - 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool's training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.
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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.