A morphing-Based 3D point cloud reconstruction framework for medical image processing

Cheng, Qiangqiang; Sun, Pengyu; Yang, Chunsheng; Yang, Yongchun; Liu, Peter

doi:10.1016/j.cmpb.2020.105495

“…3D reconstruction based on images is a common research in computer vision, medical image processing [8], and virtual reality [9]. In briefly, the following section introduces the research on the 3D reconstruction based on a single RGB image and the digestive tract reconstruction based on WCE images.…”

Section: Related Workmentioning

confidence: 99%

3D Reconstruction of Digestive Tract Based on Wireless Capsule Endoscope Images

Li

¹

,

Lu

²

,

Wang

³

et al. 2022

Advances in Transdisciplinary Engineering

0

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Based on the rapid development of 3D reconstruction technology and its expanding application in the direction of medical images, this paper proposed a research direction of 3D reconstruction of part of digestive tract based on single wireless capsule endoscope image by referring to Pixel2Mesh network. Experiments were carried out on the Data set of Kvasir-Capsule, and the results showed that compared with previous studies, the 3D model realized in this paper was more three-dimensional. At the same time, this paper provides a new idea for the development of 3D reconstruction technology in the direction of wireless capsule endoscope images.

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“…e internal points of three-dimensional graphics are identified by using the Eight Diagrams algorithm. e points discussed in this paper are composed of contour data points and internal data points on the surface of geometry by graphic files for the 3D point cloud array [13]. e three-dimensional space is divided into the eight diagrams, as shown in Figure 9.…”

Section: Internal Points Of Intracranial Hematoma Were Extractedmentioning

confidence: 99%

Optimal Design of Intracranial Hematoma Puncture Drainage Tube Based on Adaptive Bifurcation Algorithm

Chang

¹

,

Jia

²

,

Yu

³

et al. 2021

Journal of Mathematics

0

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Aiming at the puncture and drainage of clinical intracranial hematoma, we proposed an adaptive bifurcation algorithm based on the hematoma point cloud and optimized the design of the drainage tube. Firstly, based on the CT data of intracranial hematoma patients, a three-dimensional hematoma model was established, the point cloud on the surface of the hematoma was extracted and simplified, and the location of the main drainage tube was located by using the long-axis extraction algorithm. Secondly, the Eight Diagrams algorithm was used to identify the internal point cloud of hematoma, and the positions of multiple absorption points were determined by the K-means clustering algorithm. The locations of the bifurcation points of the main drainage tubes were calculated by the numerical method, and the telescopic lengths and directions of multiple subdrainage tubes were obtained. Finally, connect the main tube and the subtube, design an adaptive bifurcation drainage tube model, and apply it to intracranial hematoma puncture and drainage surgery. The algorithm can accurately determine the puncture point, puncture path, number, and location of subdrainage tubes according to the geometric characteristics of hematoma, achieve a uniform and accurate dose adjustment and drainage of intracranial hematoma, and accelerate the dissolution and drainage speed. The application of an adaptive bifurcation drainage tube can significantly reduce the risk of intracerebral hemorrhage, intracranial infection, and other complications, which has certain guiding significance and application value in clinical practice.

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“…rough the technology, CT scanned images are superimposed together, and a visual three-dimensional image model is synthesized. e model intuitively presents the shape of the tumor and the relation with the surrounding tissues, giving more accurate guidelines for preoperative evaluation and surgical planning [13][14][15]. However, there is radiation in most imaging examinations.…”

Section: Introductionmentioning

confidence: 99%

Impact Evaluation of Comfort Care on the Prognosis of Giant Thoracic Tumor Patients with Computerized Tomography Imaging under Intelligent Reconstruction Algorithm

Yan

¹

2022

Scientific Programming

0

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The reconstruction algorithm based on the network of generative adversarial and contextual coding (RANGC) was proposed in this study to analyze the impacts on the prognosis of patients with giant thoracic tumors with CT imaging under artificial intelligence algorithms. The algorithms of Feldkamp–Davis–Kress (FDK) and the generative adversarial network (GAN) were introduced. The patients were divided into the test group with comfort care and the control group with conventional care. Three sets of indicators below were also compared between the patients in two groups, including the pain level and complication incidence, the self-rating anxiety scale (SAS), the self-rating depression scale (SDS), and the patients’ satisfaction and average duration of hospital stay. When the scanning range was [0°, 89°], the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) under the RANGC algorithm were 45.6 dB and 0.99, respectively. When the scanning range changed to [0°, 119°], the PSNR and SSIM were 39.21 dB and 0.98, respectively. The results were significantly higher than those under the FDK and GAN algorithms, and the difference was obviously of statistical significance P < 0.05 . The average postoperative pain level of the patients in the control group was 3.12 points, and the postoperative complication incidence was 36.13%, while those of the test group patients were 2.27 points and 20.02%, respectively, which was greatly lower than those of the control group patients, and such a difference was of statistical significance P < 0.05 . There was no statistically significant difference in the SDS and SAS scores between the patients in the two groups before surgery. However, the SAS and SDS scores of the test group patients were 41.23 and 43.25, respectively, after surgery, which are obviously lower than those of the control group patients, with a statistically significant difference P < 0.05 . The average duration of hospital stay of the test group patients was 6.31 days, which was lower than that of the control group patients, with a statistically significant difference P < 0.05 . The overall satisfaction of the test group patients was 83.33%, which was remarkably higher than that of the control group patients, and the difference had statistical significance P < 0.05 . All these showed that the performance of the RANGC algorithm was relatively better, and comfort care did good to improve the negative mood, satisfaction, and life quality of patients after surgery.

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A morphing-Based 3D point cloud reconstruction framework for medical image processing

Cited by 31 publications

References 20 publications

3D Reconstruction of Digestive Tract Based on Wireless Capsule Endoscope Images

3D Reconstruction of Digestive Tract Based on Wireless Capsule Endoscope Images

Optimal Design of Intracranial Hematoma Puncture Drainage Tube Based on Adaptive Bifurcation Algorithm

Impact Evaluation of Comfort Care on the Prognosis of Giant Thoracic Tumor Patients with Computerized Tomography Imaging under Intelligent Reconstruction Algorithm

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