Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN

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

doi:10.1117/12.2521440

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…In clinical practice, fatty atrophy is usually classified with the Goutallier grading scale 14 and muscle waste by visual inspection or measuring cross-sectional areas (CSAs). 15,16 However, the introduction of new automated methods for segmenting and labelling the hip muscles from MR images [17][18][19][20][21] has opened the possibility of performing full 3D quantitative muscle assessment from MRI scans without the time-demanding and impractical manual labelling.…”

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confidence: 99%

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

et al. 2021

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Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle, as they are greatly involved in human daily activities. Fatty infiltration and muscle atrophy are associated with loss of strength, loss of mobility and hip disease.However, these variables have not been widely studied in this muscle group. We aimed to characterize the hip abductor muscles in a group of healthy individuals to establish reference values for volume, intramuscular fat content and shape of this muscle group. To achieve this, we executed a cross-sectional study using Dixon MRI scans of 51 healthy subjects. We used an automated segmentation method to label GMAX, GMED, GMIN and TFL muscles, measured normalized volume (NV) using lean body mass, fat fraction (FF) and lean muscle volume for each subject and computed non-parametric statistics for each variable grouped by sex and age. We measured these variables for each axial slice and created cross-sectional area and FF axial profiles for each muscle. Finally, we generated sex-specific atlases with FF statistical images. We measured median (IQR) NV values of 12.

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confidence: 99%

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

et al. 2021

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“…This work extends preliminary versions of this work presented at IFMIA2019 [3]. We build on that work by employing a newly proposed U-net for the refinement of the NMAR results in addition to the U-net for muscle segmentation.…”

Section: Introductionmentioning

confidence: 72%

“…In this paper, we combined NMAR and U-net to develop the automated muscle segmentation from a postoperative CT image contaminated by metallic artifact. This work extends preliminary versions of this work presented at IFMIA2019 [3]. We build on that work by employing a newly proposed U-net for the refinement of the NMAR results in addition to the U-net for muscle segmentation.…”

Section: Introductionmentioning

confidence: 72%

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

Sakamoto

Hiasa

Otake

et al. 2020

J Sign Process Syst

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In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NMAR), which is one of the state-of-the-art metal artifact reduction methods, and a Convolutional Neural Network-based segmentation using two U-net architectures. The first U-net refines the result of NMAR and the muscle segmentation is performed by the second U-net. We conducted experiments using simulated images of 20 patients and real images of three patients to evaluate the segmentation accuracy of 19 muscles. In simulation study, the proposed method showed statistically significant improvement (p<0.05) in the average symmetric surface distance (ASD) metric for 14 muscles out of 19 muscles and the average ASD of all muscles from 1.17 ± 0.543 mm (mean ± std over all patients) to 1.10 ± 0.509 mm over our previous method. The real image study using the manual trace of gluteus maximus and medius muscles showed ASD of 1.32 ± 0.25 mm. Our future work includes training of a network in an end-to-end manner for both the metal artifact reduction and muscle segmentation.

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“…For skeletal muscle segmentation using CT images, model-based methods have been proposed [7]. In a study by Kamiya et al, a three-dimensional (3D) shape model was used to identify the psoas major muscle, and in recent years, skeletal muscle segmentation using deep learning has been performed [8][9][10][11][12]. Hiasa and Sakamoto proposed a site-specific segmentation method for a total of 19 skeletal muscles, in the thigh and hip regions, using deep learning [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…In a study by Kamiya et al, a three-dimensional (3D) shape model was used to identify the psoas major muscle, and in recent years, skeletal muscle segmentation using deep learning has been performed [8][9][10][11][12]. Hiasa and Sakamoto proposed a site-specific segmentation method for a total of 19 skeletal muscles, in the thigh and hip regions, using deep learning [8,9]. Hashimoto et al used a 2D U-Net to identify the psoas major muscle in a low-dose CT [10], and Lee et al proposed a method for identifying skeletal muscle and fat from 2D slices in abdominal CT images using an improved fully convolutional network (FCN) [11].…”

Section: Introductionmentioning

confidence: 99%

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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Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN

Cited by 7 publications

References 2 publications

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

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

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

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