1
|
Aldieri A, Biondi R, La Mattina AA, Szyszko JA, Polizzi S, Dall'Olio D, Curti N, Castellani G, Viceconti M. Development and validation of a semi-automated and unsupervised method for femur segmentation from CT. Sci Rep 2024; 14:7403. [PMID: 38548805 PMCID: PMC10978861 DOI: 10.1038/s41598-024-57618-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/20/2024] [Indexed: 04/01/2024] Open
Abstract
Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.
Collapse
Affiliation(s)
- Alessandra Aldieri
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Riccardo Biondi
- IRCCS Bologna - Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Antonino A La Mattina
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Julia A Szyszko
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Stefano Polizzi
- Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Daniele Dall'Olio
- IRCCS Bologna - Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Nico Curti
- IRCCS Bologna - Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Physics and Astronomy, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Gastone Castellani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Marco Viceconti
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| |
Collapse
|
2
|
Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH. A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction. Tomography 2023; 9:2158-2189. [PMID: 38133073 PMCID: PMC10748093 DOI: 10.3390/tomography9060169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/27/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.
Collapse
Affiliation(s)
- Hameedur Rahman
- Department of Computer Games Development, Faculty of Computing & AI, Air University, E9, Islamabad 44000, Pakistan;
| | - Abdur Rehman Khan
- Department of Creative Technologies, Faculty of Computing & AI, Air University, E9, Islamabad 44000, Pakistan;
| | - Touseef Sadiq
- Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway
| | - Ashfaq Hussain Farooqi
- Department of Computer Science, Faculty of Computing AI, Air University, Islamabad 44000, Pakistan;
| | - Inam Ullah Khan
- Department of Electronic Engineering, School of Engineering & Applied Sciences (SEAS), Isra University, Islamabad Campus, Islamabad 44000, Pakistan;
| | - Wei Hong Lim
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia;
| |
Collapse
|