Metal Artifact Reduction In Cone-Beam Extremity Images Using Gated Convolutions

Agrawal, Harshit; Hietanen, Ari; Särkkä, Simo

doi:10.1109/isbi48211.2021.9434163

Cited by 3 publications

(2 citation statements)

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“…However, when limited by the actual situation, it is impossible to find datasets that can implement supervised algorithms. Therefore, some researchers have introduced metal masks to images without metal artifacts to generate metal artifact images, conduct supervised training, and achieve good results [39,40].…”

Section: Preprocessing Methods Used For Metal Artifacts Reduction (Mar)mentioning

confidence: 99%

“…Aiming at the spot and regular occlusion noise that are prone to appear in ultrasound medical images, this method uses total variance (TV) loss to train the neural network and successfully improves the robustness of spot and regular occlusion noise, effectively segmenting the bone features in ultrasound spine images. Agrawal, A. et al [40] simultaneously used three U-nets to segment the vertebral bodies in the coronal and sagittal planes and the pelvic region of the CT dataset and calculated the sagittal Cobb angle.…”

Section: Machine Learning Methods For 3d Imagementioning

confidence: 99%

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A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Chen

Wang

Yang

et al. 2022

Sensors

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Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.

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Section: Preprocessing Methods Used For Metal Artifacts Reduction (Mar)mentioning

confidence: 99%

Section: Machine Learning Methods For 3d Imagementioning

confidence: 99%

A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Chen

Wang

Yang

et al. 2022

Sensors

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Deep Learning Based Projection Domain Metal Segmentation for Metal Artifact Reduction in Cone Beam Computed Tomography

Agrawal,

Hietanen,

Särkkä

2023

IEEE Access

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Metal artifact correction is a challenging problem in cone beam computed tomography (CBCT) scanning. Metal implants inserted into the anatomy cause severe artifacts in reconstructed images. Widely used inpainting-based metal artifact reduction (MAR) methods require segmentation of metal traces in the projections as a first step, which is a challenging task. One approach is to use a deep learning method to segment metals in the projections. However, the success of deep learning methods is limited by the availability of realistic training data. It is laborious and time consuming to get reliable ground truth annotations due to unclear implant boundaries and large numbers of projections. We propose to use Xray simulations to generate synthetic metal segmentation training dataset from clinical CBCT scans. We compare the effect of simulations with different numbers of photons and also compare several training strategies to augment the available data. We compare our model's performance on real clinical scans with conventional region growing threshold-based MAR, moving metal artifact reduction method, and a recent deep learning method. We show that simulations with relatively small number of photons are suitable for the metal segmentation task and that training the deep learning model with full size and cropped projections together improves the robustness of the model. We show substantial improvement in the image quality affected by severe motion, voxel size under-sampling, and out-of-FOV metals. Our method can be easily integrated into the existing projection-based MAR pipeline to get improved image quality. This method can provide a novel paradigm to accurately segment metals in CBCT projections.

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