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Masad IS, Abu-Qasmieh IF, Al-Quran HH, Alawneh KZ, Abdalla KM, Al-Qudah AM. CT-based generation of synthetic-pseudo MR images with different weightings for human knee. Comput Biol Med 2024; 169:107842. [PMID: 38096761 DOI: 10.1016/j.compbiomed.2023.107842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 02/08/2024]
Abstract
Synthetic MR images are generated for their high soft-tissue contrast avoiding the discomfort by the long acquisition time and placing claustrophobic patients in the MR scanner's confined space. The aim of this study is to generate synthetic pseudo-MR images from a real CT image for the knee region in vivo. 19 healthy subjects were scanned for model training, while 13 other healthy subjects were imaged for testing. The approach used in this work is novel such that the registration was performed between the MR and CT images, and the femur bone, patella, and the surrounding soft tissue were segmented on the CT image. The tissue type was mapped to its corresponding mean and standard deviation values of the CT# of a window moving on each pixel in the reconstructed CT images, which enabled the remapping of the tissue to its MRI intrinsic parameters: T1, T2, and proton density (ρ). To generate the synthetic MR image of a knee slice, a classic spin-echo sequence was simulated using proper intrinsic and contrast parameters. Results showed that the synthetic MR images were comparable to the real images acquired with the same TE and TR values, and the average slope between them (for all knee segments) was 0.98, while the average percentage root mean square difference (PRD) was 25.7%. In conclusion, this study has shown the feasibility and validity of accurately generating synthetic MR images of the knee region in vivo with different weightings from a single real CT image.
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Affiliation(s)
- Ihssan S Masad
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan.
| | - Isam F Abu-Qasmieh
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
| | - Hiam H Al-Quran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
| | - Khaled Z Alawneh
- Department of Diagnostic Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan; King Abdullah University Hospital, Irbid, 22110, Jordan
| | - Khalid M Abdalla
- Department of Diagnostic Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan
| | - Ali M Al-Qudah
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
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Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. A review of deep learning based methods for medical image multi-organ segmentation. Phys Med 2021; 85:107-122. [PMID: 33992856 PMCID: PMC8217246 DOI: 10.1016/j.ejmp.2021.05.003] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/12/2021] [Accepted: 05/03/2021] [Indexed: 12/12/2022] Open
Abstract
Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.
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Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA.
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Wang B, Lei Y, Tian S, Wang T, Liu Y, Patel P, Jani AB, Mao H, Curran WJ, Liu T, Yang X. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Med Phys 2019; 46:1707-1718. [PMID: 30702759 DOI: 10.1002/mp.13416] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 01/18/2019] [Accepted: 01/24/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other problems. Manual segmentation can be time-consuming and is subject to inter- and intraobserver variation. We developed an automated deep learning-based method to address this technical challenge. METHODS We propose a three-dimensional (3D) fully convolutional networks (FCN) with deep supervision and group dilated convolution to segment the prostate on magnetic resonance imaging (MRI). In this method, a deeply supervised mechanism was introduced into a 3D FCN to effectively alleviate the common exploding or vanishing gradients problems in training deep models, which forces the update process of the hidden layer filters to favor highly discriminative features. A group dilated convolution which aggregates multiscale contextual information for dense prediction was proposed to enlarge the effective receptive field of convolutional neural networks, which improve the prediction accuracy of prostate boundary. In addition, we introduced a combined loss function including cosine and cross entropy, which measures similarity and dissimilarity between segmented and manual contours, to further improve the segmentation accuracy. Prostate volumes manually segmented by experienced physicians were used as a gold standard against which our segmentation accuracy was measured. RESULTS The proposed method was evaluated on an internal dataset comprising 40 T2-weighted prostate MR volumes. Our method achieved a Dice similarity coefficient (DSC) of 0.86 ± 0.04, a mean surface distance (MSD) of 1.79 ± 0.46 mm, 95% Hausdorff distance (95%HD) of 7.98 ± 2.91 mm, and absolute relative volume difference (aRVD) of 15.65 ± 10.82. A public dataset (PROMISE12) including 50 T2-weighted prostate MR volumes was also employed to evaluate our approach. Our method yielded a DSC of 0.88 ± 0.05, MSD of 1.02 ± 0.35 mm, 95% HD of 9.50 ± 5.11 mm, and aRVD of 8.93 ± 7.56. CONCLUSION We developed a novel deeply supervised deep learning-based approach with a group dilated convolution to automatically segment the MRI prostate, demonstrated its clinical feasibility, and validated its accuracy against manual segmentation. The proposed technique could be a useful tool for image-guided interventions in prostate cancer.
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Affiliation(s)
- Bo Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.,School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan, Ningxia, 750021, P.R. China
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Sibo Tian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Ashesh B Jani
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
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Yang X, Wang T, Lei Y, Higgins K, Liu T, Shim H, Curran WJ, Mao H, Nye JA. MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning. Phys Med Biol 2019; 64:025001. [PMID: 30524027 PMCID: PMC7773209 DOI: 10.1088/1361-6560/aaf5e0] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based method to derive patient-specific computed tomography (CT) maps from routine T1-weighted MRI in their native space for attenuation correction of brain PET. We developed a machine-learning-based method using a sequence of alternating random forests under the framework of an iterative refinement model. Anatomical feature selection is included in both training and predication stages to achieve optimal performance. To evaluate its accuracy, we retrospectively investigated 17 patients, each of which has been scanned by PET/CT and MR for brain. The PET images were corrected for attenuation on CT images as ground truth, as well as on pseudo CT (PCT) images generated from MR images. The PCT images showed mean average error of 66.1 ± 8.5 HU, average correlation coefficient of 0.974 ± 0.018 and average Dice similarity coefficient (DSC) larger than 0.85 for air, bone and soft tissue. The side-by-side image comparisons and joint histograms demonstrated very good agreement of PET images after correction by PCT and CT. The mean differences of voxel values in selected VOIs were less than 4%, the mean absolute difference of all active area is around 2.5%, and the mean linear correlation coefficient is 0.989 ± 0.017 between PET images corrected by CT and PCT. This work demonstrates a novel learning-based approach to automatically generate CT images from routine T1-weighted MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. Reconstructed PET images using the PCT exhibit errors well below accepted test/retest reliability of PET/CT indicating high quantitative equivalence.
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Affiliation(s)
- Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
| | - Yang Lei
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
| | - Kristin Higgins
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Hyunsuk Shim
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Walter J Curran
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Jonathon A Nye
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America
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