Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning

Liang, Xiaokun; Bibault, J.-E.; Leroy, T.; Escande, Alexandre; Zhao, Wei; Chen, Yizheng; Buyyounouski, Mark K.; Hancock, Steven; Bagshaw, H.P.; Li, Xing

doi:10.1002/mp.14755

Cited by 21 publications

(9 citation statements)

References 28 publications

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“…Liang et al. ( 17 ) developed a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from pCT to CBCT. In addition, some scholars tried to generate sCT from CBCT images, which was used to replace CBCT as the current treatment images.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Liang et al proposed a regional deformable modelbased unsupervised learning framework to automatically propagate the delineated prostate contours from planning CT to CBCT. The results showed the deep learning-based method could provide accurate contour propagation for daily CBCT-guided adaptive radiotherapy (87).…”

Section: Volumetric Imaging-based Localizationmentioning

confidence: 99%

Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

Zhao¹,

Shen²,

Islam³

et al. 2021

Quant Imaging Med Surg

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Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.

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“…Modern automatic multi-organ segmentation models can be roughly classified into two categories: conventional learning and deep learning-based segmentation. 3,[9][10][11] In general, conventional learningbased approaches for building segmentation models have two major components 12 : (a) extraction of handcrafted features to represent target organs, and (b) classification/regression model for segmentation. For instance, Glocker et al 13 developed a supervised forest model that uses both class and structural information to jointly perform pixel classification and shape regression.…”

Section: Introductionmentioning

confidence: 99%

“…Modern automatic multi‐organ segmentation models can be roughly classified into two categories: conventional learning and deep learning‐based segmentation 3,9–11 . In general, conventional learning‐based approaches for building segmentation models have two major components 12 : (a) extraction of hand‐crafted features to represent target organs, and (b) classification/regression model for segmentation.…”

Section: Introductionmentioning

confidence: 99%

A new architecture combining convolutional and transformer‐based networks for automatic 3D multi‐organ segmentation on CT images

Li,

Bagher‐Ebadian,

Sultan

et al. 2023

Medical Physics

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PurposeDeep learning‐based networks have become increasingly popular in the field of medical image segmentation. The purpose of this research was to develop and optimize a new architecture for automatic segmentation of the prostate gland and normal organs in the pelvic, thoracic, and upper gastro‐intestinal (GI) regions.MethodsWe developed an architecture which combines a shifted‐window (Swin) transformer with a convolutional U‐Net. The network includes a parallel encoder, a cross‐fusion block, and a CNN‐based decoder to extract local and global information and merge related features on the same scale. A skip connection is applied between the cross‐fusion block and decoder to integrate low‐level semantic features. Attention gates (AGs) are integrated within the CNN to suppress features in image background regions. Our network is termed “SwinAttUNet.” We optimized the architecture for automatic image segmentation. Training datasets consisted of planning‐CT datasets from 300 prostate cancer patients from an institutional database and 100 CT datasets from a publicly available dataset (CT‐ORG). Images were linearly interpolated and resampled to a spatial resolution of (1.0 × 1.0× 1.5) mm3. A volume patch (192 × 192 × 96) was used for training and inference, and the dataset was split into training (75%), validation (10%), and test (15%) cohorts. Data augmentation transforms were applied consisting of random flip, rotation, and intensity scaling. The loss function comprised Dice and cross‐entropy equally weighted and summed. We evaluated Dice coefficients (DSC), 95th percentile Hausdorff Distances (HD95), and Average Surface Distances (ASD) between results of our network and ground truth data.ResultsSwinAttUNet, DSC values were 86.54 ± 1.21, 94.15 ± 1.17, and 87.15 ± 1.68% and HD95 values were 5.06 ± 1.42, 3.16 ± 0.93, and 5.54 ± 1.63 mm for the prostate, bladder, and rectum, respectively. Respective ASD values were 1.45 ± 0.57, 0.82 ± 0.12, and 1.42 ± 0.38 mm. For the lung, liver, kidneys and pelvic bones, respective DSC values were: 97.90 ± 0.80, 96.16 ± 0.76, 93.74 ± 2.25, and 89.31 ± 3.87%. Respective HD95 values were: 5.13 ± 4.11, 2.73 ± 1.19, 2.29 ± 1.47, and 5.31 ± 1.25 mm. Respective ASD values were: 1.88 ± 1.45, 1.78 ± 1.21, 0.71 ± 0.43, and 1.21 ± 1.11 mm. Our network outperformed several existing deep learning approaches using only attention‐based convolutional or Transformer‐based feature strategies, as detailed in the results section.ConclusionsWe have demonstrated that our new architecture combining Transformer‐ and convolution‐based features is able to better learn the local and global context for automatic segmentation of multi‐organ, CT‐based anatomy.

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Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning

Cited by 21 publications

References 28 publications

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

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

Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

A new architecture combining convolutional and transformer‐based networks for automatic 3D multi‐organ segmentation on CT images

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