Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer
Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from...
Main Authors: | , , , , , , , , , , |
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Format: | Article |
Language: | English |
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SAGE Publishing
2022-07-01
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Series: | Technology in Cancer Research & Treatment |
Online Access: | https://doi.org/10.1177/15330338221105724 |
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author | Yimin Li MD Shyam Rao MD Wen Chen MD Soheila F. Azghadi MD Ky Nam Bao Nguyen MD Angel Moran MD Brittni M Usera MD Brandon A Dyer MD Lu Shang PhD Quan Chen PhD Yi Rong PhD |
author_facet | Yimin Li MD Shyam Rao MD Wen Chen MD Soheila F. Azghadi MD Ky Nam Bao Nguyen MD Angel Moran MD Brittni M Usera MD Brandon A Dyer MD Lu Shang PhD Quan Chen PhD Yi Rong PhD |
author_sort | Yimin Li MD |
collection | DOAJ |
description | Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose–volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours. |
first_indexed | 2024-04-11T23:50:07Z |
format | Article |
id | doaj.art-b9e6feef9e3d42af95d455f769118f79 |
institution | Directory Open Access Journal |
issn | 1533-0338 |
language | English |
last_indexed | 2024-04-11T23:50:07Z |
publishDate | 2022-07-01 |
publisher | SAGE Publishing |
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series | Technology in Cancer Research & Treatment |
spelling | doaj.art-b9e6feef9e3d42af95d455f769118f792022-12-22T03:56:31ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382022-07-012110.1177/15330338221105724Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck CancerYimin Li MD0Shyam Rao MD1Wen Chen MD2Soheila F. Azghadi MD3Ky Nam Bao Nguyen MD4Angel Moran MD5Brittni M Usera MD6Brandon A Dyer MD7Lu Shang PhD8Quan Chen PhD9Yi Rong PhD10 Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, , Changsha, China Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, , Portland, OR, USA Department of Radiation Oncology, , Sacramento, CA, USA Department of Radiation Oncology, City of Hope comprehensive Cancer Center, Duarte, CA, USA Department of Radiation Oncology, , Phoenix, AZ, USAPurpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose–volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.https://doi.org/10.1177/15330338221105724 |
spellingShingle | Yimin Li MD Shyam Rao MD Wen Chen MD Soheila F. Azghadi MD Ky Nam Bao Nguyen MD Angel Moran MD Brittni M Usera MD Brandon A Dyer MD Lu Shang PhD Quan Chen PhD Yi Rong PhD Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer Technology in Cancer Research & Treatment |
title | Evaluating Automatic Segmentation for Swallowing-Related Organs for Head
and Neck Cancer |
title_full | Evaluating Automatic Segmentation for Swallowing-Related Organs for Head
and Neck Cancer |
title_fullStr | Evaluating Automatic Segmentation for Swallowing-Related Organs for Head
and Neck Cancer |
title_full_unstemmed | Evaluating Automatic Segmentation for Swallowing-Related Organs for Head
and Neck Cancer |
title_short | Evaluating Automatic Segmentation for Swallowing-Related Organs for Head
and Neck Cancer |
title_sort | evaluating automatic segmentation for swallowing related organs for head and neck cancer |
url | https://doi.org/10.1177/15330338221105724 |
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