X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image

Tong, Fei; Nakao, Megumi; Wu, Shuqiong; Nakamura, Mitsuhiro; Matsuda, Tetsuya

doi:10.1109/embc44109.2020.9176655

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…It allows playback of scans as a video so that internal movement can be tracked. In this case, 3D [32,34,49,89,97,98,[117][118][119][120][121][122][123] and even 4D [59,120,[124][125][126] applications have been proposed. For example, in [120], the end-to-end DeepOrganNet framework was based on the three-variable tensor product deformation technology.…”

Section: Applications In Different Dimensionsmentioning

confidence: 99%

“…Gain spatial and temporal information in CT images [32,34,49,89,97,98,[117][118][119][120][121][122][123], [59, 120, 124- [180] proposed a Discriminative feature representation (DFR) approach with good adaptability to various CT systems because it can be directly applied to DICOM image without the need for raw measurement data. DFR outperformed iterative TV reconstruction in visual and quantitative results which showed its good robustness and performance.…”

Section: D/4dmentioning

confidence: 99%

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The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang

Yang

Shi

2022

Complex Intell. Syst.

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Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

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Section: Applications In Different Dimensionsmentioning

confidence: 99%

Section: D/4dmentioning

confidence: 99%

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang

Yang

Shi

2022

Complex Intell. Syst.

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“…A recent work [48] was the first to apply P2M to respiratory deformation estimation from a DRR, with 3D lung shapes being artificially generated from multiple initial 3D templates with free-form deformation. We previously implemented 2D/3D deformable registration methods using 4D-CT data for real patients [33], [49] and reported preliminary liver shape reconstruction results. However, in the abdominal regions, the available 2D contours or visual cues are poor.…”

Section: B Learning-based Approachesmentioning

confidence: 99%

IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration

Nakao,

Nakamura,

Matsuda

2021

Preprint

Self Cite

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Organ shape reconstruction based on a singleprojection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

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“…We want to mention several methods to obtain 3D volumes from one or more projections: While traditional simultanous algebraic reconstruction techniques (SART) require 10 or more projections to recontruct a volume [12,13], more novel methods try to use only a single projection: Wang et al [13] and Tong et al [14] used a CNN to deform 3D lung and liver templates respectively from radiographic input data. However, while one could use the estimated full 3D volume to calculate the depth, we want to note that depth estimation is a less ill-posed problem than full 3D shape estimation.…”

Section: Introductionmentioning

confidence: 99%

Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays using Convolutional Neural Networks

Schultheiß¹,

Schmette²,

Sellerer³

et al. 2021

Preprint

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Estimating the lung depth on x-ray images could provide both an accurate opportunistic lung volume estimation during clinical routine and improve image contrast in modern structural chest imaging techniques like x-ray dark-field imaging. We present a method based on a convolutional neural network that allows a per-pixel lung thickness estimation and subsequent total lung capacity estimation. The network was trained and validated using 5250 simulated radiographs generated from 525 real CT scans. Furthermore, we are able to infer the model trained with simulation data on real radiographs.For 35 patients, quantitative and qualitative evaluation was performed on standard clinical radiographs. The ground-truth for each patient's total lung volume was defined based on the patients' corresponding CT scan. The mean-absolute error between the estimated lung volume on the 35 real radiographs and groundtruth volume was 0.73 liter. Additionaly, we predicted the lung thicknesses on a synthetic dataset of 131 radiographs, where the mean-absolute error was 0.27 liter. The results show, that it is possible to transfer the knowledge obtained in a simulation model to real x-ray images.

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X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image

Cited by 18 publications

References 9 publications

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration

Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays using Convolutional Neural Networks

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