AI-based Automatic Spine CT Image Segmentation and Haptic Rendering for Spinal Needle Insertion Simulator

Park, Ikjong; Kim, Kee Hoon; Choi, Gun; Chung, Wan Kyun

doi:10.7746/jkros.2020.15.4.316

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…During the imaging process of MRI images, due to the influence of other factors, noise is inevitably generated, which also affects the accuracy of segmentation to a certain extent. Let the observed image be Y, the real image be X, and the offset field is B. en for each pixel i there is [17]as follows:…”

Section: Mr Imagesmentioning

confidence: 99%

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

Zhang

Lian

Liu

2022

Contrast Media & Molecular Imaging

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The knee joint is the second largest joint in the human body, with a wide range of functional activities and strong support for the human body. Moreover, the cartilage of the knee joint is hyaline cartilage, which is relatively brittle, so it is most vulnerable to trauma. In clinical work, the damage of articular cartilage is a disease with a high rate of orthopedic visits. In this paper, all the experimental group cases included in the observation were patients with acute articular cartilage injury or OA diagnosed by knee arthroscopy. All experimental groups and control groups did not have any strenuous exercise one day before MRI (magnetic resonance imaging), and they sat for 30 minutes before the examination. Conventional scanning sagittal FSE-T1WI, FSE-T2WI, FS-FSE-T1WI, FS-FSE-T2WI, FS-PDWI, and coronal FS-PDWI sequence. In the normal control group, after the T2 color map was generated in the workstation, the articular cartilage was divided on the midsagittal plane, and the patellar cartilage and tibial plateau were roughly divided into upper, middle, lower and anterior, middle, and posterior thirds. In order to ensure the maximum comparability of the results, an artificial intelligence segmentation algorithm is used to divide the region of interest equally, and the central part of each partition is selected as much as possible for measurement. The T2 values of the three partitions of each cartilage were measured one by one and averaged. For the comparison results of T2 value of cartilage in the same part: according to patellar cartilage, femoral cartilage, and tibial cartilage, the P values are 0.973, 0.150, and 0.525, respectively. Therefore, early detection and early treatment of articular cartilage injury are of great significance to the performance of athletes’ competition level and the extension of sports life.

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Section: Mr Imagesmentioning

confidence: 99%

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

Zhang

Lian

Liu

2022

Contrast Media & Molecular Imaging

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“…The classification segmentation network outputs the attention map, and the saliency detection network outputs the saliency map. The combination of the attention map and the saliency map can generate the network segmentation map, and the directly generated network segmentation map can be used as the final image segmentation result [13]. Here we describe how to get the network segmentation map by combining the attention map and the saliency map.…”

Section: Network Partition Diagrammentioning

confidence: 99%

Visual Intelligent Recognition System based on Visual Thinking

2021

KME

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Image semantic segmentation plays an important role and has application value in robot arm object capture, automatic driving, medical image analysis, geographic information system, etc. Aiming at the semantic segmentation method of deep learning(DL), this paper makes some attempts in the direction of weak supervised semantic segmentation, proposes AI and DL technology, and applies them to the Image segmentation technology(IST) in the construction machinery(CM) grasping task for analysis and exploration. The improvement of image weak location based on depth learning, network segmentation graph and image classification objective function are briefly analyzed. Finally, the experimental test analysis shows that a better initial attention map is not only helpful for direct segmentation, but also can promote the joint optimization performance, which verifies the effectiveness and feasibility of IST in the CM grasping task relying on AI and depth learning in this paper.

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AI-based Automatic Spine CT Image Segmentation and Haptic Rendering for Spinal Needle Insertion Simulator

Cited by 2 publications

References 17 publications

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

[Retracted] Deconstruction of Knee Cartilage Injury in Athletes Using MR Images Based on Artificial Intelligence Segmentation Algorithm

Visual Intelligent Recognition System based on Visual Thinking

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