Bayesian Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT Using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction

Sakamoto, Mitsuki; Hiasa, Yuta; Otake, Yoshito; Takao, Masaki; Suzuki, Yoshinobu; Sugano, Nobuhiko; Sato, Yoshinobu

doi:10.1007/s11265-019-01507-z

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…Such a hypothesis needs to be verified in our future work. In addition, although there were no artifacts that affected the segmentation results in the present data, there is a paper that mentions the improvement of segmentation accuracy by reducing metal artifacts in thigh muscle segmentation [21], so there is room for further investigation.…”

Section: Discussionmentioning

confidence: 65%

Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images

Kamiya

Oshima²,

Zhou³

et al. 2020

Applied Sciences

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This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.

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Section: Discussionmentioning

confidence: 65%

Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images

Kamiya

Oshima²,

Zhou³

et al. 2020

Applied Sciences

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“…Second, patients with metal implants were excluded from the analysis because these artifacts may affect the results. As our next step, we intend to accurately measure the BMD of such patients and use a deep-learning method to reduce metal artifacts 31 to provide adequate treatment of osteoporosis and prevent loosening of implants.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Uemura,

Otake,

Takashima

et al. 2023

Bone Joint Res

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AimsThis study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.MethodsThe study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.ResultsCT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm2.ConclusionAccurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery.Cite this article: Bone Joint Res 2023;12(9):590–597.

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“…Finally, multiply the new weights with higher-order features [37,38]. The size range of the updated weight is (0, 2), which can not only reduce but also expand the feature value [39].…”

Section: Attention Modulementioning

confidence: 99%

Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module

Tao

Wang

2022

Computational and Mathematical Methods in Medicine

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Traditional image segmentation methods often encounter problems of low segmentation accuracy and being time-consuming when processing complex tooth Computed Tomography (CT) images. This paper proposes an improved segmentation method for tooth CT images. Firstly, the U-Net network is used to construct a tooth image segmentation model. A large number of feature maps in downsampling are supplemented to downsampling to reduce information loss. At the same time, the problem of inaccurate image segmentation and positioning is solved. Then, the attention module is introduced into the U-Net network to increase the weight of important information and improve the accuracy of network segmentation. Among them, subregion average pooling is used instead of global average pooling to obtain spatial features. Finally, the U-Net network combined with the improved attention module is used to realize the segmentation of tooth CT images. And based on the image collection provided by West China Hospital for experimental demonstration, compared with other algorithms, our method has better segmentation performance and efficiency. The contours of the teeth obtained are clearer, which is helpful to assist the doctor in the diagnosis.

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Bayesian Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT Using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction

Cited by 14 publications

References 16 publications

Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images

Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module

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