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Arsenescu T, Chifor R, Marita T, Santoma A, Lebovici A, Duma D, Vacaras V, Badea AF. 3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation-Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography. Sensors (Basel) 2023; 23:2806. [PMID: 36905009 PMCID: PMC10007177 DOI: 10.3390/s23052806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency. Additionally, ultrasound imaging is a noninvasive diagnosis method. Artificial intelligence (AI)-based automatic segmentation of the acquired data was performed for the reconstruction and visualization of the scanned area: the carotid artery wall, the carotid artery circulated lumen, soft plaque, and calcified plaque. A qualitative evaluation was conducted via comparing the US reconstruction results with the CT angiographies of healthy and carotid-artery-disease patients. The overall scores for the automated segmentation using the MultiResUNet model for all segmented classes in our study were 0.80 for the IoU and 0.94 for the Dice. The present study demonstrated the potential of the MultiResUNet-based model for 2D-ultrasound-image automated segmentation for atherosclerosis diagnosis purposes. Using 3D ultrasound reconstructions may help operators achieve better spatial orientation and evaluation of segmentation results.
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Affiliation(s)
- Tudor Arsenescu
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
- Chifor Research SRL, 400068 Cluj-Napoca, Romania
| | - Radu Chifor
- Chifor Research SRL, 400068 Cluj-Napoca, Romania
- Department of Preventive Dentistry, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Tiberiu Marita
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Andrei Santoma
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Andrei Lebovici
- Radiology, Surgical Specialties Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
- Radiology and Imaging Department, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania
| | - Daniel Duma
- Radiology and Imaging Department, Cluj County Emergency Clinical Hospital, 400006 Cluj-Napoca, Romania
| | - Vitalie Vacaras
- Department of Neurosciences, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Neurology Department, Cluj County Emergency Hospital, 400012 Cluj-Napoca, Romania
| | - Alexandru Florin Badea
- Anatomy and Embryology, Faculty of General Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
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Chifor R, Hotoleanu M, Marita T, Arsenescu T, Socaciu MA, Badea IC, Chifor I. Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality. Sensors (Basel) 2022; 22:s22197101. [PMID: 36236200 PMCID: PMC9572264 DOI: 10.3390/s22197101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 05/28/2023]
Abstract
UNLABELLED This research aimed to evaluate Mask R-CNN and U-Net convolutional neural network models for pixel-level classification in order to perform the automatic segmentation of bi-dimensional images of US dental arches, identifying anatomical elements required for periodontal diagnosis. A secondary aim was to evaluate the efficiency of a correction method of the ground truth masks segmented by an operator, for improving the quality of the datasets used for training the neural network models, by 3D ultrasound reconstructions of the examined periodontal tissue. METHODS Ultrasound periodontal investigations were performed for 52 teeth of 11 patients using a 3D ultrasound scanner prototype. The original ultrasound images were segmented by a low experienced operator using region growing-based segmentation algorithms. Three-dimensional ultrasound reconstructions were used for the quality check and correction of the segmentation. Mask R-CNN and U-NET were trained and used for prediction of periodontal tissue's elements identification. RESULTS The average Intersection over Union ranged between 10% for the periodontal pocket and 75.6% for gingiva. Even though the original dataset contained 3417 images from 11 patients, and the corrected dataset only 2135 images from 5 patients, the prediction's accuracy is significantly better for the models trained with the corrected dataset. CONCLUSIONS The proposed quality check and correction method by evaluating in the 3D space the operator's ground truth segmentation had a positive impact on the quality of the datasets demonstrated through higher IoU after retraining the models using the corrected dataset.
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Affiliation(s)
- Radu Chifor
- Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania
- Chifor Research SRL, 400068 Cluj-Napoca, Romania
| | - Mircea Hotoleanu
- Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania
| | - Tiberiu Marita
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | | | - Mihai Adrian Socaciu
- Department of Radiology and Imaging, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400162 Cluj-Napoca, Romania
| | - Iulia Clara Badea
- Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania
| | - Ioana Chifor
- Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania
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Chifor R, Marita T, Arsenescu T, Santoma A, Badea AF, Colosi HA, Badea ME, Chifor I. Accuracy Report on a Handheld 3D Ultrasound Scanner Prototype Based on a Standard Ultrasound Machine and a Spatial Pose Reading Sensor. Sensors (Basel) 2022; 22:s22093358. [PMID: 35591048 PMCID: PMC9103853 DOI: 10.3390/s22093358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/11/2022] [Accepted: 04/25/2022] [Indexed: 02/05/2023]
Abstract
The aim of this study was to develop and evaluate a 3D ultrasound scanning method. The main requirements were the freehand architecture of the scanner and high accuracy of the reconstructions. A quantitative evaluation of a freehand 3D ultrasound scanner prototype was performed, comparing the ultrasonographic reconstructions with the CAD (computer-aided design) model of the scanned object, to determine the accuracy of the result. For six consecutive scans, the 3D ultrasonographic reconstructions were scaled and aligned with the model. The mean distance between the 3D objects ranged between 0.019 and 0.05 mm and the standard deviation between 0.287 mm and 0.565 mm. Despite some inherent limitations of our study, the quantitative evaluation of the 3D ultrasonographic reconstructions showed comparable results to other studies performed on smaller areas of the scanned objects, demonstrating the future potential of the developed prototype.
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Affiliation(s)
- Radu Chifor
- Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania; (R.C.); (M.-E.B.); (I.C.)
| | - Tiberiu Marita
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania;
- Correspondence:
| | | | - Andrei Santoma
- Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania;
| | | | - Horatiu Alexandru Colosi
- Department of Medical Education, Division of Medical Informatics and Biostatistics, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania;
| | - Mindra-Eugenia Badea
- Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania; (R.C.); (M.-E.B.); (I.C.)
| | - Ioana Chifor
- Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania; (R.C.); (M.-E.B.); (I.C.)
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Radu C, Fisher P, Mitrea D, Birlescu I, Marita T, Vancea F, Florian V, Tefas C, Badea R, Ștefănescu H, Nedevschi S, Pisla D, Hajjar NA. Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma. Biology (Basel) 2020; 9:biology9110397. [PMID: 33198415 PMCID: PMC7697343 DOI: 10.3390/biology9110397] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/23/2020] [Accepted: 11/11/2020] [Indexed: 12/19/2022]
Abstract
Simple Summary Hepatocellular carcinoma is one of the leading causes of cancer-related deaths worldwide. An image fusion system is developed for the robotic-assisted treatment of hepatocellular carcinoma, which is not only capable of imaging data interpretation and reconstruction, but also automatic tumor detection. The optimization and integration of the image fusion system within a novel robotic system has the potential to demonstrate the feasibility of the robotic-assisted targeted treatment of hepatocellular carcinoma by showing benefits such as precision, patients safety and procedure ergonomics. Abstract Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy.
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Affiliation(s)
- Corina Radu
- Regional Institute of Gastroenterology and Hepatology Prof. Dr. O.Fodor, 400162 Cluj-Napoca, Romania; (C.R.); (P.F.); (C.T.); (H.Ș.); (N.A.H.)
- Iuliu Hatieganu University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Petra Fisher
- Regional Institute of Gastroenterology and Hepatology Prof. Dr. O.Fodor, 400162 Cluj-Napoca, Romania; (C.R.); (P.F.); (C.T.); (H.Ș.); (N.A.H.)
| | - Delia Mitrea
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
| | - Iosif Birlescu
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
- Correspondence: (I.B.); (D.P.)
| | - Tiberiu Marita
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
| | - Flaviu Vancea
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
| | - Vlad Florian
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
| | - Cristian Tefas
- Regional Institute of Gastroenterology and Hepatology Prof. Dr. O.Fodor, 400162 Cluj-Napoca, Romania; (C.R.); (P.F.); (C.T.); (H.Ș.); (N.A.H.)
- Iuliu Hatieganu University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Radu Badea
- Iuliu Hatieganu University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Horia Ștefănescu
- Regional Institute of Gastroenterology and Hepatology Prof. Dr. O.Fodor, 400162 Cluj-Napoca, Romania; (C.R.); (P.F.); (C.T.); (H.Ș.); (N.A.H.)
| | - Sergiu Nedevschi
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
| | - Doina Pisla
- Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (D.M.); (T.M.); (F.V.); (V.F.); (S.N.)
- Correspondence: (I.B.); (D.P.)
| | - Nadim Al Hajjar
- Regional Institute of Gastroenterology and Hepatology Prof. Dr. O.Fodor, 400162 Cluj-Napoca, Romania; (C.R.); (P.F.); (C.T.); (H.Ș.); (N.A.H.)
- Iuliu Hatieganu University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
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