Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy
The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SI...
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| Format: | Article |
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SAGE Publishing
2025-03-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338251327081 |
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| author | Jun Li PhD Wookjin Choi PhD Rani Anne MD |
| author_facet | Jun Li PhD Wookjin Choi PhD Rani Anne MD |
| author_sort | Jun Li PhD |
| collection | DOAJ |
| description | The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, the ratio of Y-90 activity calculated using a DL-model auto-segmented liver volume to Y-90 activity calculated using a manually-delineated liver volume), were assessed. Compared with the contours generated with the Atlas method, the contours generated with the DL model had better agreement with the manually-delineated contours, which had larger DSCs (average: 0.94 ± 0.01 vs 0.83 ± 0.10) and smaller MDAs (average: 1.8 ± 0.4 mm vs 7.1 ± 5.1 mm). The average RV and average RA calculated using the DL-model-generated volumes are 0.99 ± 0.03 and 1.00 ± 0.00, respectively. The DL segmentation model was able to identify and segment livers in the CT images and provide reliable results. It outperformed the Atlas method. The model can be applied for SIRT procedures. |
| format | Article |
| id | doaj-art-67b41f862753456facbebcb0ecf391c5 |
| institution | OA Journals |
| issn | 1533-0338 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj-art-67b41f862753456facbebcb0ecf391c52025-08-20T02:10:06ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382025-03-012410.1177/15330338251327081Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation TherapyJun Li PhDWookjin Choi PhDRani Anne MDThe aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, the ratio of Y-90 activity calculated using a DL-model auto-segmented liver volume to Y-90 activity calculated using a manually-delineated liver volume), were assessed. Compared with the contours generated with the Atlas method, the contours generated with the DL model had better agreement with the manually-delineated contours, which had larger DSCs (average: 0.94 ± 0.01 vs 0.83 ± 0.10) and smaller MDAs (average: 1.8 ± 0.4 mm vs 7.1 ± 5.1 mm). The average RV and average RA calculated using the DL-model-generated volumes are 0.99 ± 0.03 and 1.00 ± 0.00, respectively. The DL segmentation model was able to identify and segment livers in the CT images and provide reliable results. It outperformed the Atlas method. The model can be applied for SIRT procedures.https://doi.org/10.1177/15330338251327081 |
| spellingShingle | Jun Li PhD Wookjin Choi PhD Rani Anne MD Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy Technology in Cancer Research & Treatment |
| title | Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy |
| title_full | Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy |
| title_fullStr | Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy |
| title_full_unstemmed | Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy |
| title_short | Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy |
| title_sort | deep learning based auto segmentation for liver yttrium 90 selective internal radiation therapy |
| url | https://doi.org/10.1177/15330338251327081 |
| work_keys_str_mv | AT junliphd deeplearningbasedautosegmentationforliveryttrium90selectiveinternalradiationtherapy AT wookjinchoiphd deeplearningbasedautosegmentationforliveryttrium90selectiveinternalradiationtherapy AT raniannemd deeplearningbasedautosegmentationforliveryttrium90selectiveinternalradiationtherapy |