• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.791-797
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• 2007

This paper proposes a semiautomatic vertebra segmentation method that overcomes limitations of both manual segmentation requiring tedious user interactions and fully automatic segmentation that is sensitive to initial conditions. The proposed method extracts fence surfaces between vertebrae, and segments a vertebra using fence-limited region growing. A fence surface is generated by a deformable model utilizing valley information in a valley emphasized Gaussian image. Fence-limited region growing segments a vertebra using gray value homogeneity and fence surfaces acting as barriers. The proposed method has been applied to ten patient data sets, and produced promising results accurately and efficiently with minimal user interaction.

• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.28-32
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• 2016

Background Congenital muscular torticollis (CMT) is a benign condition. With early diagnosis and appropriate management, it can be cured completely, leaving no residual deformity. However, long-standing, untreated CMT can lead to permanent craniofacial deformities and asymmetry.Methods Nineteen patients presented to the author with congenital muscular torticollis. Three dimensional computed tomography (3-D CT) scans was obtained upon patient’s admission. Adjustment of skull’s position to Frankfort horizontal plan was done. Cervical vertebral segmentation was done which allowed a 3D module to be separately created for each vertebra to detect any anatomical or positional changes.Results The segmented vertebrae showed an apparent anatomical changes, which were most noticeable at the level of the atlas and axis vertebrae. These changes decreased gradually till reaching the seventh cervical vertebra, which appeared to be normal in all patients. The changes in the atlas vertebra were mostly due to its intimate relation with the skull base, while the changes of the axis were the most significantConclusion Cervical vertebral segmentation is a reliable tool for isolation and studying cervical vertebral pathological changes of each vertebra separately. The accuracy of the procedures in addition to the availability of many software that can be used for segmentation will allow many surgeons to use segmentation of the vertebrae for diagnosis and even for preoperative simulation planning.

• Journal of International Society for Simulation Surgery
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• v.2 no.2
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• pp.67-70
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• 2015

We propose an automatic vessel segmentation method of vertebral arteries in CT angiography using combined circular and cylindrical model fitting. First, to generate multi-segmented volumes, whole volume is automatically divided into four segments by anatomical properties of bone structures along z-axis of head and neck. To define an optimal volume circumscribing vertebral arteries, anterior-posterior bounding and side boundaries are defined as initial extracted vessel region. Second, the initial vessel candidates are tracked using circular model fitting. Since boundaries of the vertebral arteries are ambiguous in case the arteries pass through the transverse foramen in the cervical vertebra, the circle model is extended along z-axis to cylinder model for considering additional vessel information of neighboring slices. Finally, the boundaries of the vertebral arteries are detected using graph-cut optimization. From the experiments, the proposed method provides accurate results without bone artifacts and eroded vessels in the cervical vertebra.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1140-1146
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• 2018

Objective: To compare the spinal enumeration methods that establish the first lumbar vertebra in patients with spinal variants. Materials and Methods: Of the 1446 consecutive patients who had undergone computed tomography of the spine from March 2012 to July 2016, 100 patients (62 men, 38 women; mean age, 47.9 years; age range, 19-88 years) with spinal variants were included. Two radiologists (readers 1 and 2) established the first lumbar vertebra through morphologic analysis of the thoracolumbar junction, and labeled the vertebra by counting in a cranial-to-caudal manner. Inter-observer agreement was established. Additionally, reader 1 detected the 20th vertebra under the assumption that there are 12 thoracic vertebra, and then classified it as a thoracic vertebra, lumbar vertebra, or thoracolumbar transitional vertebra (TLTV), on the basis of morphologic analysis. Results: The first lumbar vertebra, as established by morphologic analysis, was labeled by each reader as the 21st segment in 65.0% of the patients, as the 20th segment in 31.0%, and as the 19th segment in 4.0%. Inter-observer agreement between the two readers in determining the first lumbar vertebra, based on morphologic analysis, was nearly perfect (${\kappa}$ value: 1.00). The 20th vertebra was morphologically classified as a TLTV in 60.0% of the patients, as the first lumbar segment in 31.0%, as the second lumbar segment in 4.0%, and as a thoracic segment in 5.0%. Conclusion: The establishment of the first lumbar vertebra using morphologic characteristics of the thoracolumbar junction in patients with spinal variants was consistent with the morphologic traits of vertebral segmentation.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.139-146
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• 2023

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.125-130
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• 2017

Screw insertion surgery is frequently required among surgical procedures. Especially, very careful attention should be paid to the insertion of screw in the operation of the cervical vertebra. Therefore, there is a need for a guide that allows the surgeon to reliably and promptly perform treatment by calculating the desired insertion angle and length for screw insertion. In this study, the center and direction of the pedicle were calculated through 3D modeling and 3D vector numerical analysis using the CT or MRI image of the patient for the safe operation of the guide, and based on this, After that, we will implement surgical guide based on this.

• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.1041-1050
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• 2001

Control of bone cement volume (PMMA) may be critical for preventing complications in vertebroplasty, the percutaneous injection of PMMA into vertebra. The purpose of this study was to predict the optimal volume of PMMA injection based on CT images. For this, correlation between PMMA volume and textural features of CT images was examined before and after surgery to evaluate the appropriate PMMA amount. The gray level run length analysis was used to determine the textural features of the trabecular bone. Extimation of PMMA volume was done using 3D visualization with semi-automatic segmentation on postoperative CT images. Then, finite element (FE) models were constructed based on the CT image data of patients and PMMA volume. Appropriate material properties for the trabecular bone were assigned by converting BMD to elastic modulus. Structural reinforcement due to the changes in PMMA volume and BMD was assessed in terms of axial displacement of the superior endplate. A strong correlation was found between the injected PMMA volume and the area of the intertrabecular space and that of trabecular bone calculated from the CT images (r=0.90 and -0.90, respectively). FE results suggested that vertebroplasty could effectively reinforce the osteoporotic vertebra regardless of BMD or PMMA volume. Effectiveness of additional PMMA injection tended to decrease. For patients with BMD well lower than 50mg/ml, injection of up to 30% volume of the vertebral body is recommended. However, less than 30% is recommended otherwise to avoid any complications from excessive PMMA because the strength has already reached the normal level.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.376-381
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• 2019

Objective : To define optimal method that calculate the safe direction of cervical pedicle screw placement using computed tomography (CT) image based three dimensional (3D) cortical shell model of human cervical spine. Methods : Cortical shell model of cervical spine from C3 to C6 was made after segmentation of in vivo CT image data of 44 volunteers. Three dimensional Cartesian coordinate of all points constituting surface of whole vertebra, bilateral pedicle and posterior wall were acquired. The ideal trajectory of pedicle screw insertion was defined as viewing direction at which the inner area of pedicle become largest when we see through the biconcave tubular pedicle. The ideal trajectory of 352 pedicles (eight pedicles for each of 44 subjects) were calculated using custom made program and were changed from global coordinate to local coordinate according to the three dimensional position of posterior wall of each vertebral body. The transverse and sagittal angle of trajectory were defined as the angle between ideal trajectory line and perpendicular line of posterior wall in the horizontal and sagittal plane. The averages and standard deviations of all measurements were calculated. Results : The average transverse angles were $50.60^{\circ}{\pm}6.22^{\circ}$ at C3, $51.42^{\circ}{\pm}7.44^{\circ}$ at C4, $47.79^{\circ}{\pm}7.61^{\circ}$ at C5, and $41.24^{\circ}{\pm}7.76^{\circ}$ at C6. The transverse angle becomes more steep from C3 to C6. The mean sagittal angles were $9.72^{\circ}{\pm}6.73^{\circ}$ downward at C3, $5.09^{\circ}{\pm}6.39^{\circ}$ downward at C4, $0.08^{\circ}{\pm}6.06^{\circ}$ downward at C5, and $1.67^{\circ}{\pm}6.06^{\circ}$ upward at C6. The sagittal angle changes from caudad to cephalad from C3 to C6. Conclusion : The absolute values of transverse and sagittal angle in our study were not same but the trend of changes were similar to previous studies. Because we know 3D address of all points constituting cortical shell of cervical vertebrae. we can easily reconstruct 3D model and manage it freely using computer program. More creative measurement of morphological characteristics could be carried out than direct inspection of raw bone. Furthermore this concept of measurement could be used for the computing program of automated robotic screw insertion.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1055-1066
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• 2022

Objective: The clinical relevance of myosteatosis has not been well evaluated in patients with pancreatic ductal adenocarcinoma (PDAC), although sarcopenia has been extensively researched. Therefore, we evaluated the prognostic value of muscle quality, including myosteatosis, in patients with resectable PDAC treated surgically. Materials and Methods: We retrospectively evaluated 347 patients with resectable PDAC who underwent curative surgery (mean age ± standard deviation, 63.6 ± 9.6 years; 202 male). Automatic muscle segmentation was performed on preoperative computed tomography (CT) images using an artificial intelligence program. A single axial image of the portal phase at the inferior endplate level of the L3 vertebra was used for analysis in each patient. Sarcopenia was evaluated using the skeletal muscle index, calculated as the skeletal muscle area (SMA) divided by the height squared. The mean SMA attenuation was used to evaluate myosteatosis. Diagnostic cutoff values for sarcopenia and myosteatosis were devised using the Contal and O'Quigley methods, and patients were classified according to normal (nMT), sarcopenic (sMT), myosteatotic (mMT), or combined (cMT) muscle quality types. Multivariable Cox regression analyses were conducted to assess the effects of muscle type on the overall survival (OS) and recurrence-free survival (RFS) after surgery. Results: Eighty-four (24.2%), 73 (21.0%), 75 (21.6%), and 115 (33.1%) patients were classified as having nMT, sMT, mMT, and cMT, respectively. Compared to nMT, mMT and cMT were significantly associated with poorer OS, with hazard ratios (HRs) of 1.49 (95% confidence interval, 1.00-2.22) and 1.68 (1.16-2.43), respectively, while sMT was not (HR of 1.40 [0.94-2.10]). Only mMT was significantly associated with poorer RFS, with an HR of 1.59 (1.07-2.35), while sMT and cMT were not. Conclusion: Myosteatosis was associated with poor OS and RFS in patients with resectable PDAC who underwent curative surgery.