Delineation of organs at risk

Noël, Georges; Fèvre, C. Le; Antoni, D.

doi:10.1016/j.canrad.2021.08.001

Cited by 5 publications

(2 citation statements)

References 82 publications

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“…Many established dose-volume tolerance levels included in various guidelines, e.g. QUANTEC [53] , [69] or RTOG [70] , [71] , [72] , cannot be applied for hypo-fractionated regimen. Only one normal tissue complication probability model for choroidal melanoma post-proton therapy exits [42] .…”

Section: Discussionmentioning

confidence: 99%

Comparison of stereotactic radiotherapy and protons for uveal melanoma patients

Fleury,

Pignol,

Kiliç

et al. 2024

Physics and Imaging in Radiation Oncology

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Section: Discussionmentioning

confidence: 99%

Comparison of stereotactic radiotherapy and protons for uveal melanoma patients

Fleury,

Pignol,

Kiliç

et al. 2024

Physics and Imaging in Radiation Oncology

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“…The success of these treatments and the reduced toxicity highly rely on the exact definition of the different OARs ( 13 , 14 ). Radiation oncologists generally follow the established guidelines to define the volume of interest ( 15 – 17 ). The common practice is to manually draw the contours of different organs on the MR images.…”

Section: Introductionmentioning

confidence: 99%

Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images

Zhou,

Lalande,

Chevalier

et al. 2024

Front. Oncol.

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IntroductionLinear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation for abdominal tumors and organs at risk (OARs) required for the treatment planning is becoming possible. Currently, this segmentation is performed manually by radiation oncologists. This process is very time consuming and subject to inter and intra operator variabilities. In this work, deep learning based automatic segmentation solutions were investigated for abdominal OARs on 0.35 T MR-images.MethodsOne hundred and twenty one sets of abdominal MR images and their corresponding ground truth segmentations were collected and used for this work. The OARs of interest included the liver, the kidneys, the spinal cord, the stomach and the duodenum. Several UNet based models have been trained in 2D (the Classical UNet, the ResAttention UNet, the EfficientNet UNet, and the nnUNet). The best model was then trained with a 3D strategy in order to investigate possible improvements. Geometrical metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD) and analysis of the calculated volumes (thanks to Bland-Altman plot) were performed to evaluate the results.ResultsThe nnUNet trained in 3D mode achieved the best performance, with DSC scores for the liver, the kidneys, the spinal cord, the stomach, and the duodenum of 0.96 ± 0.01, 0.91 ± 0.02, 0.91 ± 0.01, 0.83 ± 0.10, and 0.69 ± 0.15, respectively. The matching IoU scores were 0.92 ± 0.01, 0.84 ± 0.04, 0.84 ± 0.02, 0.54 ± 0.16 and 0.72 ± 0.13. The corresponding HD scores were 13.0 ± 6.0 mm, 16.0 ± 6.6 mm, 3.3 ± 0.7 mm, 35.0 ± 33.0 mm, and 42.0 ± 24.0 mm. The analysis of the calculated volumes followed the same behavior.DiscussionAlthough the segmentation results for the duodenum were not optimal, these findings imply a potential clinical application of the 3D nnUNet model for the segmentation of abdominal OARs for images from 0.35 T MR-Linac.

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