Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

Tien, Hui‐Ju; Yang, Hsin Chih; Shueng, Pei‐Wei; Chen, Jyh Cheng

doi:10.1038/s41598-020-80803-2

Cited by 38 publications

(41 citation statements)

References 25 publications

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“…The discriminator (Figure 3) was a CNN that performs image classification. Its architecture was based on the PatchGAN architecture [34], considered to be the gold standard discriminator for cGAN [35,38,39]. It consisted of four consecutive ConvBlocks, with a 4 × 4 kernel size.…”

Section: Deep Convolutional Neural Network Modelsmentioning

confidence: 99%

“…Cycle GAN was first proposed for natural image synthesis. Still, recently various researchers demonstrated its application for many medical image synthesis tasks, like the generation of synthetic CT from CBCT [36][37][38][39], MR synthesis from CT images [40,41] or PET attenuation correction [42]. The main advantage of this method consists of the possibility to use unpaired, even unbalanced datasets, as the one-to-one correspondence between both dominions is no longer necessary.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Rossi

Cerveri

2021

Diagnostics

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Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

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Section: Deep Convolutional Neural Network Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Rossi

Cerveri

2021

Diagnostics

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“…This is due to the fact that addition of a loss term for segmentation may cause destructive gradient interference during backpropagation. Another recent approach 18 combined the architecture of a GAN designed to deblur images 19 with the CycleGAN architecture 12 to translate chest CBCT images to sCT images. The authors showed better results compared to CycleGAN alone 20 as well as to a residual encoder–decoder convolution neural network (RED‐CNN), 21 designed to reduce noise in low‐dose CT images.…”

Section: Introductionmentioning

confidence: 99%

Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation

et al. 2021

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Purpose In current clinical practice, noisy and artifact‐ridden weekly cone beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is performed once at the beginning of the treatment using high‐quality planning CT (pCT) images and manual contours for organs‐at‐risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid‐treatment as well as for deriving biomarkers for treatment response. Methods Using a novel physics‐based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered pCT and synthetic‐CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask three‐dimensional (3D) deep learning framework to simultaneously segment and translate real weekly CBCT images to high‐quality pCT‐like images. Results We compared the synthetic CT and OAR segmentations generated by the model to real pCT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average mean absolute error (MAE) of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D OARs segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83, and esophagus 0.66. Conclusions We demonstrate an approach to translate artifact‐ridden CBCT images to high‐quality synthetic CT images, while simultaneously generating good quality segmentation masks for different OARs. This approach could allow clinicians to adjust treatment plans using only the routine low‐quality CBCT images, potentially improving patient outcomes. Our code, data, and pre‐trained models will be made available via our physics‐based data augmentation library, Physics‐ArX, at https://github.com/nadeemlab/Physics-ArX.

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“…The detection system reported in this study yielded promising findings, as it was able to identify suspicious spots without the need for training images with abnormalities. Tien et al [ 99 ] proposed Cycle-Deblur GAN combining CycleGAN and Deblur-GAN to improve the image quality of breast-cancer patients’ chest CT images. Table 9 illustrates a brief overview of the existing studies based on GAN architecture.…”

Section: Breast-cancer-diagnosis Methods Based On Deep Learningmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

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Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

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Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

Cited by 38 publications

References 25 publications

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

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