• Annals of Surgical Treatment and Research
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• v.95 no.6
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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.

• Progress in Medical Physics
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• v.32 no.4
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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.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.166-171
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• 2010

Purpose: The SPECT/CT is able to acquire diagnostic information resolved the difficult problems that discriminate regions of focals by intergrating functional images and anatomical images. We introduce the usefulness of "Volumetrix Suite" which can describe 3D images by the convergence of the SPECT/CT images and reference CT images. Materials and Methods: We applied Volumetrix Suite program (Volumetrix IR, Volumetrix 3D) to patients, Bone, Venography, Parathyroid, WBC, taken diagnostic CT examination which have same regions of focal in Seoul Metropolitan Government Seoul National University Boramae Medical Center. After acquiring SPECT/CT images and reference CT images, we fused a couple of scans applying for this programs. The CT scan of Infinia Hawkeye 4 shows limitation of anatomical information. For this reason, we tried to transfer CT images that have lots of diagnostic informations as the form of Dicom file in PACS, and changed from 2D images to 3D images after image registering in Xeleris Workstaion of Hawkeye 4. Results & Conclusion: By using Volumetrix Suite program, we're able to acquire more accurate anatomical informations with 3D rendering which can distinguish both location and range of focals in Infinia Hawkeye 4. Thus, the result of utilizing this program indicate that nuclear medicine anatomical images can be improved by providing more diagnostic imformations produced by its program.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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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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• v.47 no.3
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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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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• v.5 no.2
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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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• v.5 no.3
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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.

• Proceedings of the KSRS Conference
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• 1999.11a
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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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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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].