ArticleJournal of applied clinical medical physics2021
Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms.
Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- A method for distance accuracy evaluation on the reformatted oblique plane of CT images.Physical and engineering sciences in medicine · 2026Article
- A multi-centre real-world evaluation of AI-assisted organ at risk contouring on radiotherapy treatment planning workflows.The British journal of radiology · 2026Article
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- Novel extended field-of-view reconstruction algorithms in computed tomography simulation for imaging and volumetric modulated arc therapy dosimetry.Quantitative imaging in medicine and surgery · 2026Article
- 3DAD: Super-Resolution Image Synthesis from Anisotropic CT Images Using a Three-Dimensional Adversarial Diffusion Model.Bioengineering (Basel, Switzerland) · 2026Article
- The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study.International journal of computer assisted radiology and surgery · 2026Article
- A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.Nature communications · 2026Observational
- Technical Development and Implementation of 3D-QALAS on a 1.5T MR-Linac for the Brain: A Prospective R-IDEAL Stage 0/1 Technology Development Report.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a Real Surgical Database.Plastic and reconstructive surgery. Global open · 2026Article
- Voxel Normalization in LDCT Imaging: Its Significance in Texture Feature Selection for Pulmonary Nodule Malignancy Classification: Insights from Two Centers.Diagnostics (Basel, Switzerland) · 2026Article
- Characterizing the effects of noncontrast head CT reconstruction kernel and slice thickness parameters on the performance of an automated AI algorithm in the evaluation of ischemic stroke.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Comparison of Low-Dose, Standard-Dose, and High-Definition CBCT Protocols for STL Reconstruction Accuracy in Digital Bone Scaffold Workflows.International journal of dentistry · 2026Article
- Robust automated method of spatial resolution measurement in radiotherapy CT simulation images.Journal of applied clinical medical physics · 2025Article
- A Method for Sensitivity Analysis of Automatic Contouring Algorithms Across Different MRI Contrast Weightings Using SyntheticMR.medRxiv : the preprint server for health sciences · 2025Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Cardiac substructure delineation in radiation therapy - A state-of-the-art review.Journal of medical imaging and radiation oncology · 2024Review
- Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis.3D printing in medicine · 2024Article
- Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.Journal of applied clinical medical physics · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
purposeTo investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms.
methodsEleven scans from patients with head-and-neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low-dose simulation software. The impact of these imaging parameters on two in-house auto-contouring algorithms, one convolutional neural network (CNN)-based and one multiatlas-based system (MACS) was investigated for 183 reconstructed scans. For each algorithm, auto-contours for organs-at-risk were compared with auto-contours from scans with 3 mm slice thickness, 0.977 mm pixel size, and 100% CT dose using Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD).
resultsIncreasing the slice thickness from baseline value of 3 mm gave a progressive reduction in DSC and an increase in HD and MSD on average for all structures. Reducing the CT dose only had a relatively minimal effect on DSC and HD. The rate of change with respect to dose for both auto-contouring methods is approximately 0. Changes in pixel size had a small effect on DSC and HD for CNN-based auto-contouring with differences in DSC being within 0.07. Small structures had larger deviations from the baseline values than large structures for DSC. The relative differences in HD and MSD between the large and small structures were small.
conclusionsAuto-contours can deviate substantially with changes in CT acquisition and reconstruction parameters, especially slice thickness and pixel size. The CNN was less sensitive to changes in pixel size, and dose levels than the MACS. The results contraindicated more restrictive values for the parameters should be used than a typical imaging protocol for head-and-neck.
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