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Li X, Tian Y, Li S, Dai Y, Chen Y, Li L. Optimization analysis of surgical lumen instrument cleaning management path under the background of medical big data. Minerva Gastroenterol (Torino) 2024; 70:133-135. [PMID: 37477170 DOI: 10.23736/s2724-5985.23.03452-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Affiliation(s)
- Xiaohua Li
- Sterilization and Supply Center, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui, China
| | - Yuquan Tian
- Operating Room, Shandong Provincial Third Hospital, Jinan, Shandong, China
| | - Suting Li
- Teaching and Research Office, Binzhou Polytechnic Department of Internal Medicine, Binzhou, Shandong, China
| | - Ying Dai
- Sterilization and Supply Center, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui, China
| | - Yufeng Chen
- Sterilization and Supply Center, Yijishan Hospital, Wannan Medical College, Wuhu, Anhui, China
| | - Li Li
- Sterilization and Supply Center, The Third People's Hospital of Liaocheng City, Liaocheng, Shandong, China -
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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Su B, Wang Z, Gong Y, Li M, Teng Y, Yu S, Zong Y, Yao W, Wang J. Anal center detection and classification of perianal healthy condition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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Blanco PJ, Ziemer PGP, Bulant CA, Ueki Y, Bass R, Räber L, Lemos PA, García-García HM. Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets. Med Image Anal 2021; 75:102262. [PMID: 34670148 DOI: 10.1016/j.media.2021.102262] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 10/20/2022]
Abstract
Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm2, [1.1,-1.5]mm2, while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.
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Affiliation(s)
- Pablo J Blanco
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil.
| | - Paulo G P Ziemer
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Carlos A Bulant
- Consejo Nacional de Investigaciones Científicas, CONICET, Argentina; Universidad Nacional del Centro, UNICEN, Tandil, Argentina; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Yasushi Ueki
- Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ronald Bass
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA
| | - Lorenz Räber
- Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pedro A Lemos
- Hospital Israelita Albert Einstein, São Paulo, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Héctor M García-García
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA.
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Tian F, Gao Y, Fang Z, Gu J. Automatic coronary artery segmentation algorithm based on deep learning and digital image processing. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02197-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Ziemer PGP, Bulant CA, Orlando JI, Maso Talou GD, Álvarez LAM, Guedes Bezerra C, Lemos PA, García-García HM, Blanco PJ. Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2020; 1:75-82. [PMID: 36713961 PMCID: PMC9707866 DOI: 10.1093/ehjdh/ztaa014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/23/2020] [Accepted: 11/06/2020] [Indexed: 02/01/2023]
Abstract
Aims Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. Methods and results First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs) to regress the final lumen boundary. The dataset consisted of 85 IVUS pullbacks (52 patients). The dataset was partitioned at the pullback-level using 73 pullbacks for training (20 586 frames), 6 pullbacks for validation (1692 frames), and 6 for testing (1692 frames). The degree of overlapping, between the ground truth and ML contours, median (interquartile range, IQR) systematically increased from 0.896 (0.874-0.933) for MF1 to 0.925 (0.911-0.948) for MF11. The median (IQR) of the distance error was also reduced from 3.83 (2.94-4.98)% for MF1 to 3.02 (2.25-3.95)% for MF11-GP. The corresponding median (IQR) in the lumen area error remained between 5.49 (2.50-10.50)% for MF1 and 5.12 (2.15-9.00)% for MF11-GP. The dispersion in the relative distance and area errors consistently decreased as we increased the number of frames, and also when the GP regressor was coupled to the MFCNN output. Conclusion These results demonstrate that the proposed ML approach is suitable to effectively segment the lumen boundary in IVUS scans, reducing the burden of costly and time-consuming manual delineation.
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Affiliation(s)
- Paulo G P Ziemer
- National Laboratory for Scientific Computing, Av. Getúlio Vargas 333, 25651-075 Petrópolis, Brazil,National Institute of Science and Technology in Medicine Assisted by Scientific Computing, Petrópolis, Brazil
| | - Carlos A Bulant
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, Petrópolis, Brazil,National Scientific and Technical Research Council, CONICET and Pladema Institute, National University of the Center of the Buenos Aires Province, Tandil, Argentina
| | - José I Orlando
- National Scientific and Technical Research Council, CONICET and Pladema Institute, National University of the Center of the Buenos Aires Province, Tandil, Argentina
| | - Gonzalo D Maso Talou
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Luis A Mansilla Álvarez
- National Laboratory for Scientific Computing, Av. Getúlio Vargas 333, 25651-075 Petrópolis, Brazil,National Institute of Science and Technology in Medicine Assisted by Scientific Computing, Petrópolis, Brazil
| | - Cristiano Guedes Bezerra
- Department of Interventional Cardiology, Heart Institute (InCor) and the University of São Paulo Medical School, São Paulo, Brazil
| | - Pedro A Lemos
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, Petrópolis, Brazil,Department of Interventional Cardiology, Heart Institute (InCor) and the University of São Paulo Medical School, São Paulo, Brazil,Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Héctor M García-García
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA,Georgetown University School of Medicine, Washington, DC, USA,Corresponding authors. Tel: +55 24 2233 6067, (P.J.B.); Tel: +1 202877 7754, ; (H.M.G.-G.)
| | - Pablo J Blanco
- National Laboratory for Scientific Computing, Av. Getúlio Vargas 333, 25651-075 Petrópolis, Brazil,National Institute of Science and Technology in Medicine Assisted by Scientific Computing, Petrópolis, Brazil,Corresponding authors. Tel: +55 24 2233 6067, (P.J.B.); Tel: +1 202877 7754, ; (H.M.G.-G.)
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