Personalized brachytherapy dose reconstruction using deep learning

Akhavanallaf, Azadeh; Mohammadi, Reza; Shiri, Isaac; Salimi, Yazdan; Arabi, Hossein; Zaidi, Habib

doi:10.1016/j.compbiomed.2021.104755

Cited by 33 publications

(17 citation statements)

References 37 publications

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“…24 Recently, deep convolutional neural network (DCNN) has been applied to radiotherapy dose calculation or prediction. 15,[28][29][30][31][32][33][34] Promising results were reported that dose comparable to MCS could be obtained with higher efficiency for brachytherapy. 15,28,31,34 However, the reported DCNN methods have not fully considered the ISA or tissue heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…Although these methods can improve the dose accuracy compared to TG‐43, they are unable to fully account for the impact of tissue heterogeneity and do not adequately address ISA 24 . Recently, deep convolutional neural network (DCNN) has been applied to radiotherapy dose calculation or prediction 15,28–34 . Promising results were reported that dose comparable to MCS could be obtained with higher efficiency for brachytherapy 15,28,31,34 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine

Xiao,

Xiong,

Geng

et al. 2023

Medical Physics

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BackgroundSeed implant brachytherapy (SIBT) is an effective treatment modality for head and neck (H&N) cancers; however, current clinical planning requires manual setting of needle paths and utilizes inaccurate dose calculation algorithms.PurposeThis study aims to develop an accurate and efficient deep convolutional neural network dose engine (DCNN‐DE) and an automatic SIBT planning method for H&N SIBT.MethodsA cohort of 25 H&N patients who received SIBT was utilized to develop and validate the methods. The DCNN‐DE was developed based on 3D‐unet model. It takes single seed dose distribution from a modified TG‐43 method, the CT image and a novel inter‐seed shadow map (ISSM) as inputs, and predicts the dose map of accuracy close to the one from Monte Carlo simulations (MCS). The ISSM was proposed to better handle inter‐seed attenuation. The accuracy and efficacy of the DCNN‐DE were validated by comparing with other methods taking MCS dose as reference. For SIBT planning, a novel strategy inspired by clinical practice was proposed to automatically generate parallel or non‐parallel potential needle paths that avoid puncturing bone and critical organs. A heuristic‐based optimization method was developed to optimize the seed positions to meet clinical prescription requirements. The proposed planning method was validated by re‐planning the 25 cases and comparing with clinical plans.ResultsThe absolute percentage error in the TG‐43 calculation for CTV V100 and D90 was reduced from 5.4% and 13.2% to 0.4% and 1.1% with DCNN‐DE, an accuracy improvement of 93% and 92%, respectively. The proposed planning method could automatically obtain a plan in 2.5 ± 1.5 min. The generated plans were judged clinically acceptable with dose distribution comparable with those of the clinical plans.ConclusionsThe proposed method can generate clinically acceptable plans quickly with high accuracy in dose evaluation, and thus has a high potential for clinical use in SIBT.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine

Xiao,

Xiong,

Geng

et al. 2023

Medical Physics

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“…Recently, deep learning approaches, including supervised, unsupervised, and semi-supervised methods, are employed in different medical image analysis tasks [ 18 – 28 ], different BT tasks [ 29 – 31 ], and learn and carry out spatial alignment/transformation between images [ 32 – 41 ]. These methods usually used convolutional neural networks (CNNs) to extract informative features automatically to perform this task [ 32 – 41 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer

et al. 2022

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In this study, an inter-fraction organ deformation simulation framework for the locally advanced cervical cancer (LACC), which considers the anatomical flexibility, rigidity, and motion within an image deformation, was proposed. Data included 57 CT scans (7202 2D slices) of patients with LACC randomly divided into the train (n = 42) and test (n = 15) datasets. In addition to CT images and the corresponding RT structure (bladder, cervix, and rectum), the bone was segmented, and the coaches were eliminated. The correlated stochastic field was simulated using the same size as the target image (used for deformation) to produce the general random deformation. The deformation field was optimized to have a maximum amplitude in the rectum region, a moderate amplitude in the bladder region, and an amplitude as minimum as possible within bony structures. The DIRNet is a convolutional neural network that consists of convolutional regressors, spatial transformation, as well as resampling blocks. It was implemented by different parameters. Mean Dice indices of 0.89 ± 0.02, 0.96 ± 0.01, and 0.93 ± 0.02 were obtained for the cervix, bladder, and rectum (defined as at-risk organs), respectively. Furthermore, a mean average symmetric surface distance of 1.61 ± 0.46 mm for the cervix, 1.17 ± 0.15 mm for the bladder, and 1.06 ± 0.42 mm for the rectum were achieved. In addition, a mean Jaccard of 0.86 ± 0.04 for the cervix, 0.93 ± 0.01 for the bladder, and 0.88 ± 0.04 for the rectum were observed on the test dataset (15 subjects). Deep learning-based non-rigid image registration is, therefore, proposed for the high-dose-rate brachytherapy in inter-fraction cervical cancer since it outperformed conventional algorithms.

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“…Artificial intelligence (AI) algorithms, particularly its two major subcategories, machine learning (ML), and DL, have been widely used for medical image analysis 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 and more recently in the segmentation of lung and pneumonia infectious lesions from chest CT images of COVID‐19 patients. 15 These studies reported that AI improved the accuracy of lesion detection/segmentation and reduced the bias associated with conventional approaches.…”

Section: Introductionmentioning

confidence: 99%

“…19 Furthermore, developing a fully automatic tool for lung and pneumonia COVID-19 lesions is highly desired owing to rapid changes in appearance and manifestation at different stages of the disease. 13,19 Artificial intelligence (AI) algorithms, particularly its two major subcategories, machine learning (ML), and DL, have been widely used for medical image analysis [24][25][26][27][28][29][30][31] and more recently in the segmentation of lung and pneumonia infectious lesions from chest CT images of COVID-19 patients. 15 These studies reported that AI improved the accuracy of lesion detection/segmentation and reduced the bias associated with conventional approaches.…”

Section: Introductionmentioning

confidence: 99%

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

Shiri

Arabi

Salimi

et al. 2021

Int J Imaging Syst Tech

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We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non‐square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID‐19 lesions segmentation was evaluated on an external reverse transcription‐polymerase chain reaction positive COVID‐19 dataset (7′333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98–0.99) and 0.91 ± 0.038 (95% CI, 0.90–0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, −0.12 to 0.18) and −0.18 ± 3.4% (95% CI, −0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16–0.59) and 0.81 ± 6.6% (95% CI, −0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first‐order feature (−6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL‐guided three‐dimensional whole lung and infected regions segmentation in COVID‐19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

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Personalized brachytherapy dose reconstruction using deep learning

Cited by 33 publications

References 37 publications

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine

Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

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