• 로봇학회논문지
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• 제15권4호
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• pp.316-322
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• 2020

Endoscopic spine surgery is an advanced surgical technique for spinal surgery since it minimizes skin incision, muscle damage, and blood loss compared to open surgery. It requires, however, accurate positioning of an endoscope to avoid spinal nerves and to locate the endoscope near the target disk. Before the insertion of the endoscope, a guide needle is inserted to guide it. Also, the result of the surgery highly depends on the surgeons' experience and the patients' CT or MRI images. Thus, for the training, a number of haptic simulators for spinal needle insertion have been developed. But, still, it is difficult to be used in the medical field practically because previous studies require manual segmentation of vertebrae from CT images, and interaction force between the needle and soft tissue has not been considered carefully. This paper proposes AI-based automatic vertebrae CT-image segmentation and haptic rendering method using the proposed need-tissue interaction model. For the segmentation, U-net structure was implemented and the accuracy was 93% in pixel and 88% in IoU. The needle-tissue interaction model including puncture force and friction force was implemented for haptic rendering in the proposed spinal needle insertion simulator.

• 대한의용생체공학회:의공학회지
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• 제44권5호
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• pp.354-361
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• 2023

Most of spinal diseases are diagnosed based on the subjective judgment of a specialist, so numerous studies have been conducted to find objectivity by automating the diagnosis process using deep learning. In this paper, we propose a method that combines segmentation and feature extraction, which are frequently used techniques for diagnosing spinal diseases. Four models, U-Net, U-Net++, DeepLabv3+, and M-Net were trained and compared using 1000 X-ray images, and key-points were derived using Douglas-Peucker algorithms. For evaluation, Dice Similarity Coefficient(DSC), Intersection over Union(IoU), precision, recall, and area under precision-recall curve evaluation metrics were used and U-Net++ showed the best performance in all metrics with an average DSC of 0.9724. For the average Euclidean distance between estimated key-points and ground truth, U-Net was the best, followed by U-Net++. However the difference in average distance was about 0.1 pixels, which is not significant. The results suggest that it is possible to extract key-points based on segmentation and that it can be used to accurately diagnose various spinal diseases, including spondylolisthesis, with consistent criteria.

• 대한의용생체공학회:의공학회지
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• 제44권2호
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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.

• 한국CDE학회논문집
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• 제17권6호
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• pp.436-442
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• 2012

Traumatic loading during car accidents or sports activities can lead to cervical spinal cord injury. Experiments in spinal cord injury research are mainly carried out on rabbit or rat. Finite element models that include the rat cervical spinal cord and adjacent soft tissues should be developed for efficient studies of mechanisms of spinal cord injury. Images of a rat were obtained from high resolution MRI scanner. Polygonal surfaces were extracted structure by structure from the MRI data using the ITK-SNAP volume segmentation software. These surfaces were converted to Non-uniform Rational B-spline surfaces by the INUS Rapidform rapid prototyping software. Rapidform was also used to generate a thin shell surface model for the dura mater which sheathes the spinal cord. Altair's Hypermesh pre-processor was used to generate finite element meshes for each structure. These processes in this study can be utilized in modeling of other biomedical tissues and can be one of examples for reverse engineering on biomechanics.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 1999년도 Japanese Journal of Medical Physics
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• pp.162-163
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• 1999

• 한국방사선학회논문지
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• 제16권7호
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• pp.879-887
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• 2022

본 연구에서는 체압 측정형 침대의 기계식 순차 상승 방식이 실제로 척추의 부분별 분절운동을 유도하는지 확인하고자 하였다. 이를 위해 측면 X-선 검사를 하여 순차적 압력을 주는 장치가 척추의 각 부분에 기계적 수직 상승을 주어 척추의 계단식 분절을 유도함을 확인하였다. 이후 요통을 인지하고 있는 대상자에게 통증, 보행 능력, 우울 척도를 측정하고 분석하였다. 10일 동안의 시각적 상사 척도(p<0.05), 요통 장애지수(p<0.05)는 침대 사용 후 평균이 감소하는 경향을 보였다. 보행 능력 검사(p<0.05)에서는 침대 사용 횟수가 증가함에 따라 검사에서의 이동 시간이 감소하였으며 이동 거리는 증가하였다. 또한, 침대 사용 후 노인 우울 척도(p<0.05)가 감소함을 나타냈다. 그 결과, 침대가 제공하는 온열과 지압으로 인한 척추의 분절은 통증의 완화와 더불어 보행과 우울감에도 영향을 미치는 것을 확인하였다.

• Radiation Oncology Journal
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• 제37권2호
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• pp.134-142
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• 2019

Purpose: The aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours. Methods: A sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM). Results: The mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures. Conclusion: Improved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.

• 대한전자공학회논문지TC
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• 제46권12호
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• pp.102-107
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• 2009

기술의 발달로 의료장비의 현대화가 이루어지고 PACS와 같은 시스템이 보편화되면서 디지털 의료영상처리 기술에 대한 관심이 높아지고 있다. 영역분할 기술은 디지털의료영상처리에서 첫 번째 단계로 필요한 전처리기술이다. 영역분할을 통하여 특정 부위가 종양, 부종, 파손 및 괴사세포와 같은 이상 현상을 나타내는 것을 조기에 발견할 수 있도록 해주고, 의사들이 적절한 처방을 내려줄 수 있도록 도와줄 수 있다. 특히 두뇌영상에서 백질, 회백질 및 CSF(cerebral spinal fluid)의 영역분할은 두뇌연구의 핵심기술이다. 이들 의료영상에서 기존의 윤곽선이나 영역 확장법은 애매한 경계선과 장기내의 물리적 특성이 비균질하여 영역분할의 실패율을 높게 한다. 퍼지기반의 영역분할 알고리듬은 불분명한 경계를 이루는 장기의 영역분할에 강하다고 알려져 있다. 본 연구에서는 자기공명영상이 강하게 나타내는 잡음에도 안정적인 퍼지기반의 영역분할 알고리듬을 제안하였다. 제안된 알고리듬은 이웃화소들을 군집시킬 때에 평균과 분산의 정보를 이용하여 최소한의 계산을 추가함으로써, 기존의 퍼지기반 영역분할 방법에 비하여 실패율이 대략 30% 이하로 낮은 것을 확인하였다.

• Korean Journal of Radiology
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• 제19권6호
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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.

• Korean Journal of Radiology
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• 제25권4호
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.