Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer

Mohammadi, Reza; Shokatian, Iman; Salehi, Mohammad; Arabi, Hossein; Shiri, Isaac; Zaidi, Habib

doi:10.1016/j.radonc.2021.03.030

Cited by 63 publications

(46 citation statements)

References 61 publications

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“…For instance, AI-based segmentation algorithms can accurately delineate the treatment target, organs at risk, and brachytherapy applicators and seeds for commonly used imaging modalities in brachytherapy, such as CT, MRI, and ultrasound. [1][2][3] AI methods for dose calculations have also become available to enable calculations with tissue heterogeneity considered. 4 Compared to external beam RT, there are unique challenges in brachytherapy.…”

Section: Xun Jia Phdmentioning

confidence: 99%

Artificial intelligence can overcome challenges in brachytherapy treatment planning

Jia

Cunha

Rong

2022

J Applied Clin Med Phys

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Section: Xun Jia Phdmentioning

confidence: 99%

Artificial intelligence can overcome challenges in brachytherapy treatment planning

Jia

Cunha

Rong

2022

J Applied Clin Med Phys

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“…Segmentation task has a significant portion of medical image analyses. Attention to different medical image segmentation tasks remarkably increased in the research community 8 Organ segmentation, 9 COVID-19 Lung/Lesion segmentation, 10 fetal head segmentation, 11 fetal ultrasound image segmentation for fetal biometric parameters measurements, 12 and tumor segmentation. 13…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

Chenarlogh¹,

Oghli

Shabanzadeh³

et al. 2022

Ultrason Imaging

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U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.

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“…AI and machine learning (ML) have demonstrated great performance in various medical fields and have proven their vital role in complicated therapeutic scenes. These systems have shown high level of accuracy in different applications, such as lung disease classification, breast cancer, skin lesion classification, identifying diabetic retinopathy, and Alzheimer [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

Zaeri¹

2022

Simulation Modeling

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The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

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Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer

Cited by 63 publications

References 61 publications

Artificial intelligence can overcome challenges in brachytherapy treatment planning

Artificial intelligence can overcome challenges in brachytherapy treatment planning

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

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