The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation

Velde, Jan Van de; Wouters, Jan; Vercauteren, Tom; Gersem, Werner De; Achten, Eric; Neve, Wilfried De; Hoof, Tom Van

doi:10.1186/s13014-015-0570-x

Cited by 2 publications

(3 citation statements)

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“…Schipaanboord et al 48 and Van de Velde et al 49 both showed that when atlas selection strategies are used to select atlases most similar to the anatomy of the patient being segmented, performance is significantly improved over using a random selection of atlases. This concept can logically be extended to support the IPP atlas-based autosegmentation method used in the current paper; using prior images from the same patient as atlases achieves the maximal similarity between the atlases and the image being segmented.…”

Section: Discussionmentioning

confidence: 99%

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Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers

et al. 2023

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BackgroundIn order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)‐linear accelerator (MR‐linac), the low‐resolution T2‐weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction.PurposeIn this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on‐board setup MRIs from the MR‐linac for off‐line reconstruction of delivered dose.MethodsSeven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1–9) atlas‐based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10–19) autosegmentation using images from a patient's 1–4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter‐observer variability using Dunn's test with control. Methods were compared pairwise using the Steel‐Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high‐performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low‐performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics.ResultsDL and IPP methods performed best overall, all significantly outperforming inter‐observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter‐observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7–13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314).ConclusionsThe autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on‐board T2‐weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end‐to‐end dose accumulation workflow.

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Section: Discussionmentioning

confidence: 99%

“…Schipaanboord et al 48 . and Van de Velde et al 49 . both showed that when atlas selection strategies are used to select atlases most similar to the anatomy of the patient being segmented, performance is significantly improved over using a random selection of atlases.…”

Section: Discussionmentioning

confidence: 99%

Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Schipaanboord et al [46] and Van de Velde et al [47] both showed that when atlas selection strategies are used to select atlases most similar to the anatomy of the patient being segmented, performance is significantly improved over using a random selection of atlases. This concept can logically be extended to support the IPP atlas-based autosegmentation method used in the current paper; using prior images from the same patient as atlases achieves the maximal similarity between the atlases and the image being segmented.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Autosegmentation Techniques on T2-Weighted MRI for Off-line Dose Reconstruction in MR-Linac Adapt to Position Workflow for Head and Neck Cancers

McDonald

Cárdenas

O’Connell

et al. 2021

Preprint

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Purpose: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. In this study, our goal is to evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. 20 autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior (IPP)) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance, Hausdorff distance, and Jaccard index. For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions (IPP_RF_4), IPP with 1 fraction (IPP_1)), and one low-performing (PAL with STAPLE and 5 atlases (PAL_ST_5)). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. Results: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 seconds per case) and PAL methods the slowest (3.7 - 13.8 minutes per case). Execution time increased with number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

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The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation

Cited by 2 publications

References 15 publications

Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers

Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers

Investigation of Autosegmentation Techniques on T2-Weighted MRI for Off-line Dose Reconstruction in MR-Linac Adapt to Position Workflow for Head and Neck Cancers

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