End‐to‐end unsupervised cycle‐consistent fully convolutional network for 3D pelvic CT‐MR deformable registration

Guo, Yi; Wu, Xiangyi; Wang, Zhi; Xi, Peng; Xu, Xintao

doi:10.1002/acm2.12968

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…To deal with this problem, we predict both Φ M →F and Φ F →M and we use both for the optimization of our network. Different methods such as [10,6] explore similar concepts using however different networks for each deformation. Due to our fusion strategy on the encoding part, our approach is able to learn both transformations with less parameters.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Estienne

Vakalopoulou

Battistella

et al. 2021

Segmentation, Classification, and Registration of Multi-Modality Medical Imaging Data

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Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of 0.64 for task 3 and 0.85 for task 4 on the test sets, taking third place on the challenge.

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Section: Methodsmentioning

confidence: 99%

Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Estienne

Vakalopoulou

Battistella

et al. 2021

Segmentation, Classification, and Registration of Multi-Modality Medical Imaging Data

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“…Six ResNet Blocks ( 27 ) were used to increase the depth of the network and make the model easier to optimize. The loss function of the registration included the MIND (modality-independent neighborhood descriptor) loss (L MIND ) ( 28 , 29 ) and smoothing loss (L smooth ) ( 30 ). The model was trained and tested on Nvidia Geforce RTX 3090.…”

Section: Methodsmentioning

confidence: 99%

A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality

Yang

Chang²,

Liang³

et al. 2022

Front. Oncol.

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PurposeThe aim of this study is to compare two methods for improving the image quality of the Varian Halcyon cone-beam CT (iCBCT) system through the deformed planning CT (dpCT) based on the convolutional neural network (CNN) and the synthetic CT (sCT) generation based on the cycle-consistent generative adversarial network (CycleGAN).MethodsA total of 190 paired pelvic CT and iCBCT image datasets were included in the study, out of which 150 were used for model training and the remaining 40 were used for model testing. For the registration network, we proposed a 3D multi-stage registration network (MSnet) to deform planning CT images to agree with iCBCT images, and the contours from CT images were propagated to the corresponding iCBCT images through a deformation matrix. The overlap between the deformed contours (dpCT) and the fixed contours (iCBCT) was calculated for purposes of evaluating the registration accuracy. For the sCT generation, we trained the 2D CycleGAN using the deformation-registered CT-iCBCT slicers and generated the sCT with corresponding iCBCT image data. Then, on sCT images, physicians re-delineated the contours that were compared with contours of manually delineated iCBCT images. The organs for contour comparison included the bladder, spinal cord, femoral head left, femoral head right, and bone marrow. The dice similarity coefficient (DSC) was used to evaluate the accuracy of registration and the accuracy of sCT generation.ResultsThe DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder (p < 0.05), 0.765 and 0.850 for the spinal cord (p < 0.05), 0.918 and 0.923 for the femoral head left (p > 0.05), 0.916 and 0.921 for the femoral head right (p > 0.05), and 0.878 and 0.916 for the bone marrow (p < 0.05), respectively. When the bladder volume difference in planning CT and iCBCT scans was more than double, the accuracy of sCT generation was significantly better than that of registration (DSC of bladder: 0.859 vs. 0.596, p < 0.05).ConclusionThe registration and sCT generation could both improve the iCBCT image quality effectively, and the sCT generation could achieve higher accuracy when the difference in planning CT and iCBCT was large.

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“…Therefore, in addition to common network training, complex pre-processing procedures are required to achieve accurate registration. For example, Guo et al (134) applied rigid registration before using the DL model to reduce motion amplitudes, whereas other groups applied binary masks to focus on ROIs (38,115). Fu et al segmented and increased the intensity of pulmonary vessels by a factor of 1,000 to enrich the image details (38).…”

Section: Discussionmentioning

confidence: 99%

“…To better regularize DVFs, some researchers modified the training strategy to a cycle-consistent way. To achieve this, they processed the warped image through the network and transformed it back to the moving image (112,113,134). This strategy reduced the number of negative values in the Jaco.…”

Section: Similarity Metric-based Registrationmentioning

confidence: 99%

A review of deep learning-based three-dimensional medical image registration methods

Xiao¹,

Teng²,

Liu³

et al. 2021

Quant Imaging Med Surg

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Medical image registration is a vital component of many medical procedures, such as imageguided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research.The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.

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End‐to‐end unsupervised cycle‐consistent fully convolutional network for 3D pelvic CT‐MR deformable registration

Cited by 9 publications

References 24 publications

Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality

A review of deep learning-based three-dimensional medical image registration methods

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