A novel class of machine-learning-driven real-time 2D/3D tracking methods: texture model registration (TMR)

Steininger, Philipp; Neuner, Markus; Fritscher, Karl D.; Sedlmayer, Felix; Deutschmann, H.

doi:10.1117/12.878147

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…The second group of experimental reference images is set to (−3,4,2,10,10,10). Finally, the third group of experimental reference images is set to (5,6,7,8,9,10). In the experiment, the initial value is optimized by (0,0,0,0,0,0).…”

Section: Experimental Evaluation Criteriamentioning

confidence: 99%

“…Image-guided surgery, which involves computer vision, biomedicine, imaging, automatic control, and other disciplines, is an interdisciplinary research direction [1][2][3][4][5]. Through the comprehensive application of a variety of medical image information, it carries out the preoperative diagnosis, disease analysis, planning of surgical path, intraoperative localization of the lesion, real-time tracking of surgical instruments, and adjustment of the spatial position of surgical instruments to achieve an accurate diagnosis [6,7].…”

Section: Introductionmentioning

confidence: 99%

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2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

et al. 2022

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Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

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Section: Experimental Evaluation Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

et al. 2022

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“…Numerous methods and applications of image-based 3D-2D registration (alternatively 2D-3D registration, making no claim as to the order or which constitutes the moving or fixed image) have been reported at the MI104 conference, with the term broadly applied to video-to-volume registration (e.g., endoscopy to CT), slice-to-volume registration (e.g., ultrasound to MRI), and projection-to-volume registration (e.g., fluoroscopy to CT). 48 – 57 Such work includes novel methods and implementations for 3D-2D registration with applications ranging from needle interventions to catheter guidance and orthopedic surgery. Prominent among these are methods for registration of 3D CT (or CBCT) to intraoperative 2D fluoroscopy, with many groups reporting research on novel objective functions, motion models (including piecewise rigid registration), and optimization methods.…”

Section: Major Themesmentioning

confidence: 99%

SPIE Medical Imaging 50th anniversary: historical review of the Image-Guided Procedures, Robotic Interventions, and Modeling conference

Siewerdsen

Linte

2022

J. Med. Imag.

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. Purpose Among the conferences comprising the Medical Imaging Symposium is the MI104 conference currently titled Image-Guided Procedures, Robotic Interventions, and Modeling, although its name has evolved through at least nine iterations over the last 30 years. Here, we discuss the important role that this forum has presented for researchers in the field during this time. Approach The origins of the conference are traced from its roots in Image Capture and Display in the late 1980s, and some of the major themes for which the conference and its proceedings have provided a valuable forum are highlighted. Results These major themes include image display/visualization, surgical tracking/navigation, surgical robotics, interventional imaging, image registration, and modeling. Exceptional work from the conference is highlighted by summarizing keynote lectures, the top 50 most downloaded proceedings papers over the last 30 years, the most downloaded paper each year, and the papers earning student paper and young scientist awards. Conclusions Looking forward and considering the burgeoning technologies, algorithms, and markets related to image-guided and robot-assisted interventions, we anticipate growth and ever increasing quality of the conference as well as increased interaction with sister conferences within the symposium.

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“…Image-guided surgery, which involves computer vision, biomedicine, imaging, automatic control, and other disciplines, is an interdisciplinary research direction [1]. Through the comprehensive application of a variety of medical image information [2], it carries out the preoperative diagnosis [3][4][5][6][7], disease analysis [8,9], planning of the surgical path, intraoperative localization of the lesion, real-time tracking of surgical instruments [10], and adjustment of the spatial position of surgical instruments to achieve an accurate diagnosisso as to provide groundbreaking and precise treatment [11]. This technology provides many benefits for patients, such as reducing surgical trauma, speeding up recovery, and reducing hospital stay and costs.…”

Section: Introductionmentioning

confidence: 99%

2D/3D Multimode Medical Image Alignment Based on Spatial Histograms

et al. 2022

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The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algorithms. The results show that the proposed algorithm can improve accuracy and efficiency, and reduce the initial value’s sensitivity.

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A novel class of machine-learning-driven real-time 2D/3D tracking methods: texture model registration (TMR)

Cited by 4 publications

References 10 publications

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

SPIE Medical Imaging 50th anniversary: historical review of the Image-Guided Procedures, Robotic Interventions, and Modeling conference

2D/3D Multimode Medical Image Alignment Based on Spatial Histograms

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