Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm

Kim, Jung Jin; Nam, Jimin; Jang, In Gwun

doi:10.1016/j.cmpb.2017.11.007

Cited by 34 publications

(18 citation statements)

References 40 publications

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“…This study performed FE analysis to quantitatively investigate the effect of the estimated BMD on the structural behavior; the reference BMD was used as the ground truth. Segmentation of spine and hip images was first conducted using ITK-SNAP 3.6.0 [30]; hole filling and surface smoothing were implemented as postprocessing [31]. Then, each voxel in the segmented images was directly converted to a corresponding 8-node solid element.…”

Section: Methodsmentioning

confidence: 99%

Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study

Lee

Kim

Jang

2019

Computational and Mathematical Methods in Medicine

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Objectives This study proposes a regression model for the phantomless Hounsfield units (HU) to bone mineral density (BMD) conversion including patient physical factors and analyzes the accuracy of the estimated BMD values. Methods The HU values, BMDs, circumferences of the body, and cross-sectional areas of bone were measured from 39 quantitative computed tomography images of L2 vertebrae and hips. Then, the phantomless HU-to-BMD conversion was derived using a multiple linear regression model. For the statistical analysis, the correlation between the estimated BMD values and the reference BMD values was evaluated using Pearson's correlation test. Voxelwise BMD and finite element analysis (FEA) results were analyzed in terms of root-mean-square error (RMSE) and strain energy density, respectively. Results The HU values and circumferences were statistically significant (p < 0.05) for the lumbar spine, whereas only the HU values were statistically significant (p < 0.05) for the proximal femur. The BMD values estimated using the proposed HU-to-BMD conversion were significantly correlated with those measured using the reference phantom: Pearson's correlation coefficients of 0.998 and 0.984 for the lumbar spine and proximal femur, respectively. The RMSEs of the estimated BMD values for the lumbar spine and hip were 4.26 ± 0.60 (mg/cc) and 8.35 ± 0.57 (mg/cc), respectively. The errors of total strain energy were 1.06% and 0.91%, respectively. Conclusions The proposed phantomless HU-to-BMD conversion demonstrates the potential of precisely estimating BMD values from CT images without the reference phantom and being utilized as a viable tool for FEA-based quantitative assessment using routine CT images.

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Section: Methodsmentioning

confidence: 99%

Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study

Lee

Kim

Jang

2019

Computational and Mathematical Methods in Medicine

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“…From the CT scan data, a 3D skeletal image for the target bone was segmented by using the complementary relationship between the watershed algorithm and optimal thresholding . This scheme can effectively preserve thin cortical bone and trabecular compartments during segmentation despite the low resolution and noise of the CT images.…”

Section: Methodsmentioning

confidence: 99%

Computational study of estimating 3D trabecular bone microstructure for the volume of interest from CT scan data

Kim

Nam

Jang

2018

Numer Methods Biomed Eng

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Inspired by the self-optimizing capabilities of bone, a new concept of bone microstructure reconstruction has been recently introduced by using 2D synthetic skeletal images. As a preliminary clinical study, this paper proposes a topology optimization-based method that can estimate 3D trabecular bone microstructure for the volume of interest (VOI) from 3D computed tomography (CT) scan data with enhanced computational efficiency and phenomenological accuracy. For this purpose, a localized finite element (FE) model is constructed by segmenting a target bone from CT scan data and determining the physiological local loads for the VOI. Then, topology optimization is conducted with multiresolution bone mineral density (BMD) deviation constraints to preserve the patient-specific spatial bone distribution obtained from the CT scan data. For the first time, to our knowledge, this study has demonstrated that 60-μm resolution trabecular bone images can be reconstructed from 600-μm resolution CT scan data (a 62-year-old woman with no metabolic bone disorder) for the 4 VOIs in the proximal femur. The reconstructed trabecular bone includes the characteristic trabecular patterns and has morphometric indices that are in good agreement with the anatomical data in the literature. As for computational efficiency, the localization for the VOI reduces the number of FEs by 99%, compared with that of the full FE model. Compared with the previous single-resolution BMD deviation constraint, the proposed multiresolution BMD deviation constraints enable at least 65% and 47% reductions in the number of iterations and computing time, respectively. These results demonstrate the clinical feasibility and potential of the proposed method.

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“…Various methods have been proposed to solve these problems (Table 1 ) [ 4 , 9 , 10 ]. Deep learning methods, particularly deep convolutional neural network (CNN)-based methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty

et al. 2022

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Purpose Preoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomography (CT), pelvis and femur must be segmented. Accurate and rapid segmentation of the hip joint is challenging. This study aimed to develop a novel deep learning network, named Changmugu Net (CMG Net), which could achieve accurate segmentation of the femur and pelvis. Methods The overall deep neural network architecture of CMG Net employed three interrelated modules. CMG Net included the 2D U-net to separate the bony and soft tissues. The modular hierarchy method was used for the main femur segmentation to achieve better performance. A layer classifier was adopted to localise femur layers among a series of CT scan images. The first module was a modified 2D U-net, which separated bony and soft tissues; it provided intermediate supervision for the main femur segmentation. The second module was the main femur segmentation, which was used to distinguish the femur from the acetabulum. The third module was the layer classifier, which served as a post-processor for the second module. Results There was a much greater overlap in accuracy results with the “gold standard” segmentation than with competing networks. The dice overlap coefficient was 93.55% ± 5.57%; the mean surface distance was 1.34 ± 0.24 mm, and the Hausdorff distance was 4.19 ± 1.04 mm in the normal and diseased hips, which indicated greater accuracy than the other four competing networks. Moreover, the mean segmentation time of CMG Net was 25.87 ± 2.73 s, which was shorter than the times of the other four networks. Conclusions The prominent segmentation accuracy and run-time of CMG Net suggest that it is a reliable method for clinicians to observe anatomical structures of the hip joints, even in severely diseased cases.

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Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm

Cited by 34 publications

References 40 publications

Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study

Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study

Computational study of estimating 3D trabecular bone microstructure for the volume of interest from CT scan data

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty

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