Automatic segmentation of three clinical target volumes in radiotherapy using lifelong learning

Men, Kuo; Chen, Xinyuan; Yang, Baiyu; Zhu, Ji; Yi, Junlin; Wang, Shulian; Li, Yexiong; Dai, Jianrong

doi:10.1016/j.radonc.2020.12.034

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Automatic segmentation is often applied in CBCT-based image-guided radiotherapy to improve treatment efficiency. Our previously published network was adopted for segmentation in this study ( 33 ). The model is being applied to assist the radiation oncologists in daily clinical work, which have helped them to save time.…”

Section: Methodsmentioning

confidence: 99%

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

et al. 2022

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PurposeThe challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.Materials and methodsIn this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder–decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.ResultsTransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.ConclusionsThe proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.

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Section: Methodsmentioning

confidence: 99%

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

et al. 2022

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“…Due to the limited number of available datasets, a fivefold cross-validation was used to test prediction model performance. The prediction performance was quantitatively evaluated by ( 1 ) the voxel-wise mean absolute error (MAE) and ( 2 ) the dice similarity coefficient (DSC) of isodose volumes. Isodoses from 10 Gy to 160 Gy were evaluated with a 10-Gy interval.…”

Section: Methodsmentioning

confidence: 99%

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning–based dose prediction

Fu,

Chen,

Liu

et al. 2024

Front. Oncol.

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PurposeDifficulties remain in dose optimization and evaluation of cervical cancer radiotherapy that combines external beam radiotherapy (EBRT) and brachytherapy (BT). This study estimates and improves the accumulated dose distribution of EBRT and BT with deep learning–based dose prediction.Materials and methodsA total of 30 patients treated with combined cervical cancer radiotherapy were enrolled in this study. The dose distributions of EBRT and BT plans were accumulated using commercial deformable image registration. A ResNet-101–based deep learning model was trained to predict pixel-wise dose distributions. To test the role of the predicted accumulated dose in clinic, each EBRT plan was designed using conventional method and then redesigned referencing the predicted accumulated dose distribution. Bladder and rectum dosimetric parameters and normal tissue complication probability (NTCP) values were calculated and compared between the conventional and redesigned accumulated doses.ResultsThe redesigned accumulated doses showed a decrease in mean values of V50, V60, and D2cc for the bladder (−3.02%, −1.71%, and −1.19 Gy, respectively) and rectum (−4.82%, −1.97%, and −4.13 Gy, respectively). The mean NTCP values for the bladder and rectum were also decreased by 0.02‰ and 0.98%, respectively. All values had statistically significant differences (p < 0.01), except for the bladder D2cc (p = 0.112).ConclusionThis study realized accumulated dose prediction for combined cervical cancer radiotherapy without knowing the BT dose. The predicted dose served as a reference for EBRT treatment planning, leading to a superior accumulated dose distribution and lower NTCP values.

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“…However, the basic step for prediction is to accurately identify the residual tumor region or the tumor bed [ 14 ]. In general, the procedure is delineated manually by the radiologists on medical software, which is labor intensive and time-consuming [ 15 ]. As the essential modality of rectal cancer, T2-weighted imaging (T2WI) can display anatomical information with a clearer tumor boundary by high spatial resolution [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework

Xia,

Li,

Zhu

et al. 2024

BMC Cancer

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Purpose Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. Methods A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. Results At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680–0.720), 17.73 mm (95% CI: 16.08–19.39), and 3.11 mm (95% CI: 2.67–3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). Conclusions The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

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Automatic segmentation of three clinical target volumes in radiotherapy using lifelong learning

Cited by 11 publications

References 24 publications

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning–based dose prediction

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework

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