The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer

Guo, Hongbo; Wang, Jiazhou; Xia, Xiang; Yang, Zhong; Peng, Jiayuan; Zhang, Zhen; Hu, Weigang

doi:10.1186/s13014-021-01837-y

“…The two main ideas behind them are: (1) a pixel-wise comparison of ground-truth and predicted segmentation and (2) measuring the distance between the ground-truth and the predicted contours. What carries a higher relevance in clinical practice, however, is the dosimetric accuracy and the quality of the treatment plans that can be achieved on the basis of the predicted segmentations [ 12 , 20 ]. At the time of writing, no studies exist that have investigated and quantified the dosimetric impact of CT organ delineations for prostate cancer patients obtained from deep CNNs.…”

Section: Introductionmentioning

confidence: 99%

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

Kawula

¹

,

Purice

²

,

Li

³

et al. 2022

View full text Add to dashboard Cite

Background The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients. Methods A database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated. Results 3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V$$_{60/65/70\,{\mathrm{Gy}}}$$ 60 / 65 / 70 Gy for the bladder and V$$_{50/65/70\,{\mathrm{Gy}}}$$ 50 / 65 / 70 Gy for the rectum, showed agreement between dose distributions within $$\pm \, 5\%$$ ± 5 % and $$\pm \,2\%$$ ± 2 % , respectively. The D$$_{98/2\%}$$ 98 / 2 % and V$$_{95\%}$$ 95 % , for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.

show abstract

“…Guo et al conducted the dosimetric of OARs between their in-house and a learning-based commercial auto-segmentation system (United Imaging Healthcare) with manual contouring. They found no significant difference for most cases in PTV and OAR doses [ 45 ]. In this study, we employed nn U-net, a self-adapting ensemble method comparable to a commercial system, for simultaneous multi-organ contouring in gynecological brachytherapy.…”

Section: Discussionmentioning

confidence: 99%

A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy

Li

¹

,

Zhu

²

,

Zhang

³

et al. 2022

View full text Add to dashboard Cite

Purpose Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment planning process and the high dose gradient around the HRCTV. This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological cancer. Materials and methods We applied nnU-Net (no new U-Net), an automatically adapted deep convolutional neural network based on U-Net, to segment the bladder, rectum and HRCTV on CT images in gynecological cancer. In nnU-Net, three architectures, including 2D U-Net, 3D U-Net and 3D-Cascade U-Net, were trained and finally ensembled. 207 cases were randomly chosen for training, and 30 for testing. Quantitative evaluation used well-established image segmentation metrics, including dice similarity coefficient (DSC), 95% Hausdorff distance (HD95%), and average surface distance (ASD). Qualitative analysis of automated segmentation results was performed visually by two radiation oncologists. The dosimetric evaluation was performed by comparing the dose-volume parameters of both predicted segmentation and human contouring. Results nnU-Net obtained high qualitative and quantitative segmentation accuracy on the test dataset and performed better than previously reported methods in bladder and rectum segmentation. In quantitative evaluation, 3D-Cascade achieved the best performance in the bladder (DSC: 0.936 ± 0.051, HD95%: 3.503 ± 1.956, ASD: 0.944 ± 0.503), rectum (DSC: 0.831 ± 0.074, HD95%: 7.579 ± 5.857, ASD: 3.6 ± 3.485), and HRCTV (DSC: 0.836 ± 0.07, HD95%: 7.42 ± 5.023, ASD: 2.094 ± 1.311). According to the qualitative evaluation, over 76% of the test data set had no or minor visually detectable errors in segmentation. Conclusion This work showed nnU-Net’s superiority in segmenting OARs and HRCTV in gynecological brachytherapy cases in our center, among which 3D-Cascade shows the highest accuracy in segmentation across different applicators and patient anatomy.

show abstract

“…In recent years, with the wide application of low-dose CT scanning methods in the field of imaging diagnosis, the denoising methods of low-dose CT images based on deep learning have become a hot issue in this field (38,39). In this study, we proposed a CNCycle-GAN network to solve the noise and artifacts in low-dose CT images of the abdominal and pelvis.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors

Gong

¹

,

Yao

²

,

Ni

³

et al. 2022

View full text Add to dashboard Cite

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world’s first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose. Reducing the tube current of the FBCT system can reduce the scanning dose, but it will lead to serious noise and artifacts in the reconstructed images. In this study, we proposed a deep learning method, Content-Noise Cycle-Consistent Generative Adversarial Network (CNCycle-GAN), to improve the image quality and CT value accuracy of low-dose FBCT images to meet the requirements of adaptive radiotherapy. We selected 76 patients with abdominal and pelvic tumors who received radiation therapy. The patients received one low-dose CT scan and one normal-dose CT scan in IGRT mode during different fractions of radiotherapy. The normal dose CT images (NDCT) and low dose CT images (LDCT) of 70 patients were used for network training, and the remaining 6 patients were used to validate the performance of the network. The quality of low-dose CT images after network restoration (RCT) were evaluated in three aspects: image quality, automatic delineation performance and dose calculation accuracy. Taking NDCT images as a reference, RCT images reduced MAE from 34.34 ± 5.91 to 20.25 ± 4.27, PSNR increased from 34.08 ± 1.49 to 37.23 ± 2.63, and SSIM increased from 0.92 ± 0.08 to 0.94 ± 0.07. The P value is less than 0.01 of the above performance indicators indicated that the difference were statistically significant. The Dice similarity coefficients (DCS) between the automatic delineation results of organs at risk such as bladder, femoral heads, and rectum on RCT and the results of manual delineation by doctors both reached 0.98. In terms of dose calculation accuracy, compared with the automatic planning based on LDCT, the difference in dose distribution between the automatic planning based on RCT and the automatic planning based on NDCT were smaller. Therefore, based on the integrated CT-linac platform, combined with deep learning technology, it provides clinical feasibility for the realization of low-dose FBCT adaptive radiotherapy for abdominal and pelvic tumors.

show abstract

The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer

Cited by 26 publications

References 30 publications

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors

Contact Info

Product

Resources

About