• Annals of Surgical Treatment and Research
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• 제95권6호
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• pp.297-302
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• 2018

Purpose: Increased robotic surgery is attended by increased reports of complications, largely due to limited operative view and lack of tactile sense. These kinds of obstacles, which seldom occur in open surgery, are challenging for beginner surgeons. To enhance robotic surgery safety, we created an augmented reality (AR) model of the organs around the thyroid glands, and tested the AR model applicability in robotic thyroidectomy. Methods: We created AR images of the thyroid gland, common carotid arteries, trachea, and esophagus using preoperative CT images of a thyroid carcinoma patient. For a preliminary test, we overlaid the AR images on a 3-dimensional printed model at five different angles and evaluated its accuracy using Dice similarity coefficient. We then overlaid the AR images on the real-time operative images during robotic thyroidectomy. Results: The Dice similarity coefficients ranged from 0.984 to 0.9908, and the mean of the five different angles was 0.987. During the entire process of robotic thyroidectomy, the AR images were successfully overlaid on the real-time operative images using manual registration. Conclusion: We successfully demonstrated the use of AR on the operative field during robotic thyroidectomy. Although there are currently limitations, the use of AR in robotic surgery will become more practical as the technology advances and may contribute to the enhancement of surgical safety.

• 한국의학물리학회지:의학물리
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• 제32권4호
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• pp.172-178
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• 2021

Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility. Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics. Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant. Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.

• 핵의학기술
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• 제14권2호
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• pp.166-171
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• 2010

SPECT/CT는 SPECT의 기능학적 영상과 CT의 해부학적인 영상의 융합(Fusion)을 통하여 기존의 SPECT에서 병소의 위치 및 범위를 감별하는데 어려웠던 문제들을 해결하여 진단적 정보에 도움을 줄수 있다. Infinia Hawkeye 4 (GE Healthcare)의 SPECT/CT 감마카메라 영상과 진단용 CT영상을 융합(Fusion)하여 3D (three Dimension)로 Rendering 할 수 있는 Volumetrix Suite의 유용성을 소개하고자 한다. 본원에 SPECT/CT 검사를 하기 위해 내원한 환자 중, 동일한 검사부위의 진단용 CT영상이 있는 환자(Bone, Venography, Parathyroid, WBC)를 대상으로 Volumetrix Suite (Volumetrix IR, Volumetrix 3D)를 적용하였다. Infinia Hawkeye 4의 SPECT영상과 CT영상을 획득한 후 두 영상을 2D (two Dimension)로 융합(Fusion)하였다. Infinia Hawkeye4 SPECT/CT에서의 CT는 해부학적 정보에 한계가 있어, 3D (three Dimension) Rendering을 하기 위해서는 정보량이 많은 진단용 CT영상을 PACS상에서 DICOM (Digital Imaging and Communications in Medicine) File로 전송하여 Infinia Hawkeye4의 Xeleris Workstation에서 IR (Image Registration)한 후 Intergrating 3D (three Dimension)로 융합(Fusion)하여 2D(two Dimension)영상을 3D (three Dimension)영상으로 Rendering하였다. Volumetrix Suite program을 이용함으로써 Infinia Hawkeye 4의 SPECT/CT 영상과 별도로 촬영한 고해상도 진단용 CT영상을 3D Rendering하여 병소의 위치 및 범위를 감별하는데 좀 더 명확한 해부학적 정보를 얻을 수 있었다. 따라서, 해부학적인 정보가 부족한 핵의학영상을 보완함으로서 더 많은 정보를 제공해 핵의학영상 검사의 진단능력을 향상시킬 수 있을 것이라고 기대된다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.279-280
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• 1998

A new algorithm is developed that can reduce the metal artifact on CT caused by pedicle screws. Metal artifact has been recognized as a major problem in precise reading of CT images. In particular, spine surgeons have been bothered with the artifact appearing on CT taken after pedicle screw insertion. To reduce the artifact, our new algorithm first finds the center line from CT images, and then overlays an exact size screw image on the CT. The exact screw is obtained from an actual design specifications of screw, and the CT images are processed to maximize bone margins while minimizing screw images through adjusting the window width and level. 실험 결과 단순한 Window W/L 조절로는 해결되지 않는군요. This algorithm provides spine surgeons with more accurate CT images and thus better interpretation of CT to ascertain the success or failure of pedicle screw insertion.

• Journal of the Korean Wood Science and Technology
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• 제47권3호
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• pp.265-276
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• 2019

In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of $128{\times}128{\times}3pixels$ via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3.

• 한국측량학회지
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• 제40권5호
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• pp.439-448
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• 2022

MV (Moving Vehicle) detection using satellite imagery is important for traffic monitoring and provides a wide range of observations. Specifically, MV detection methods utilizing the time lag in single-pass optical satellite images have been studied for detecting MVs from a single set of images. Because of limitations in detecting MVs outside of roads, most previous studies required road information to limit the moving object to cars on the road. However, it is difficult to obtain road information from inaccessible areas. Therefore, this study proposed a new method for detecting MVs regardless of their locations from single-pass optical satellite images without using additional data. WV-3 (Worldview-3) satellite images were used, and a spatial correlation coefficient map was proposed to detect spatial displacement which denotes MVs across two WV-3 MS images. Finally, evaluation was performed through quantitative metrics and visual inspection. The evaluation results revealed that the proposed method can detect MV movements from the single-pass satellite images. On the contrary, misdetected or undetected MVs due to radiometric differences between the images could be identified by visual inspection. The performance of the proposed method can be improved by minimizing radiometric variations and adding conditions that are robust to radiometric differences between the images.

• Journal of the Optical Society of Korea
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• 제5권2호
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• pp.43-48
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• 2001

A three-dimensional (3D) display system based on computer-generated integral photography (CGIP) is proposed and its feasibility is discussed. Instead of the pickup process in conventional If, the elemental images of imaginary objects are computer-generated. Using these images, we observed autostereoscopic 3D images in full color and full parallax. The lateral and depth resolutions of the integrated images are limited by some factors such as the image position, object thickness, the lens width, and the pixel size of display panel.

• Journal of the Optical Society of Korea
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• 제5권3호
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• pp.117-122
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• 2001

A three-dimensional (3D) display system based on computer-generated integral photography (CGIP) is proposed and its feasibility is discussed. Instead of the pickup process in conventional IP, the elemental images of imaginary objects are computer-generated. Using these images, we observed autostereoscopic 3D images in full color and full parallax. The lateral and depth resolutions of the integrated images are limited by some factors such as the image position, object thickness, the lens width, and the pixel size of display panel.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.405-410
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• 1999

Topographic maps can be made and updated with satellite images, but it requires many human interactions that are inefficient and costly. Therefore, the automatizing of the road extraction procedures could increase efficiency in terms of time and cost. Although methods of extracting roads, railroads and rivers from satellite images have been developed in many studies, studies on the road extraction from satellite images of urbanized area are still not relevant, because many artificial components In the city makes the delineation of the roads difficult. So, to extract roads from high resolution satellite images of urbanized area, this study has proposed the combined use of wavelet transform and multi-resolution analysis. In consequence, this study verifies that it is possible to automatize the road extraction from satellite images of urbanized area. And to realize the automatization more completely, various algorithms need to be developed.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.148-153
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• 2009

Few methods have dealt with segmenting multiple images with analogous content. Concurrent images of a scene and gathered images of a similar foreground are examples of these images, which we term consistent scene images. In this paper, we present a method to segment these images based on manual segmentation of one image, by iteratively propagating information via multi-level cues with adaptive confidence. The cues are classified as low-, mid-, and high- levels based on whether they pertain to pixels, patches, and shapes. Propagated cues are used to compute potentials in an MRF framework, and segmentation is done by energy minimization. Through this process, the proposed method attempts to maximize the amount of extracted information and maximize the consistency of segmentation. We demonstrate the effectiveness of the proposed method on several sets of consistent scene images and provide a comparison with results based only on mid-level cues [1].