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Rouhi R, Niyoteka S, Carré A, Achkar S, Laurent PA, Ba MB, Veres C, Henry T, Vakalopoulou M, Sun R, Espenel S, Mrissa L, Laville A, Chargari C, Deutsch E, Robert C. Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer. Phys Imaging Radiat Oncol 2024; 30:100578. [PMID: 38912007 PMCID: PMC11192799 DOI: 10.1016/j.phro.2024.100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 04/08/2024] [Accepted: 04/08/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Purpose Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features. Methods and materials We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient ( DSC ) < T and DSC ⩾ T . Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing. Results Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC (SDSC 3 mm ), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16,SDSC 3 mm =0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation ( M =0) on the test cohort. Failure detection could generate precision ( P = 0.88 ), recall ( R = 0.75 ), F1-score ( F = 0.81 ), and accuracy ( A = 0.86 ) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values. Conclusions Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.
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Affiliation(s)
- Rahimeh Rouhi
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Stéphane Niyoteka
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Alexandre Carré
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Samir Achkar
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Pierre-Antoine Laurent
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Mouhamadou Bachir Ba
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
- Radiotherapy Department of the University Hospital Center of Dalal Jamm, Guédiawaye, Senegal
| | - Cristina Veres
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Théophraste Henry
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Medical Imaging, Gustave Roussy Cancer Campus, Villejuif, France
| | - Maria Vakalopoulou
- Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Roger Sun
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Sophie Espenel
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Linda Mrissa
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Adrien Laville
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
| | - Cyrus Chargari
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Eric Deutsch
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Charlotte Robert
- Université Paris-Saclay, Institut Gustave Roussy, Inserm, Radiothérapie Moléculaire et Innovation Thérapeutique, 94800 Villejuif, France
- Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
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Taso M, Aramendía-Vidaurreta V, Englund EK, Francis S, Franklin S, Madhuranthakam AJ, Martirosian P, Nayak KS, Qin Q, Shao X, Thomas DL, Zun Z, Fernández-Seara MA. Update on state-of-the-art for arterial spin labeling (ASL) human perfusion imaging outside of the brain. Magn Reson Med 2023; 89:1754-1776. [PMID: 36747380 DOI: 10.1002/mrm.29609] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/09/2023] [Accepted: 01/16/2023] [Indexed: 02/08/2023]
Abstract
This review article provides an overview of developments for arterial spin labeling (ASL) perfusion imaging in the body (i.e., outside of the brain). It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group. In this review, we focus on specific challenges and developments tailored for ASL in a variety of body locations. After presenting common challenges, organ-specific reviews of challenges and developments are presented, including kidneys, lungs, heart (myocardium), placenta, eye (retina), liver, pancreas, and muscle, which are regions that have seen the most developments outside of the brain. Summaries and recommendations of acquisition parameters (when appropriate) are provided for each organ. We then explore the possibilities for wider adoption of body ASL based on large standardization efforts, as well as the potential opportunities based on recent advances in high/low-field systems and machine-learning. This review seeks to provide an overview of the current state-of-the-art of ASL for applications in the body, highlighting ongoing challenges and solutions that aim to enable more widespread use of the technique in clinical practice.
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Affiliation(s)
- Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Erin K Englund
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Susan Francis
- Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham, UK
| | - Suzanne Franklin
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Center for Image Sciences, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Ananth J Madhuranthakam
- Department of Radiology, Advanced Imaging Research Center, and Biomedical Engineering, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Petros Martirosian
- Section on Experimental Radiology, Department of Radiology, University Hospital of Tuebingen, Tuebingen, Germany
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T 1 and T 2 Relaxation Times with Application to Cancer Cell Culture. Int J Mol Sci 2023; 24:ijms24021554. [PMID: 36675075 PMCID: PMC9861169 DOI: 10.3390/ijms24021554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023] Open
Abstract
Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.
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Mirgun Yalcinkaya D, Youssef K, Heydari B, Zamudio L, Dharmakumar R, Sharif B. Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4072-4078. [PMID: 34892124 PMCID: PMC9949517 DOI: 10.1109/embc46164.2021.9629581] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.
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Affiliation(s)
- Dilek Mirgun Yalcinkaya
- Cedars-Sinai Medical Center, UCLA Dept. of Bioengineering, and the Laboratory for Translational Imaging of Microcirculation, Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis
| | - Khalid Youssef
- Cedars-Sinai Medical Center, UCLA Dept. of Bioengineering, and the Laboratory for Translational Imaging of Microcirculation, Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis
| | - Bobby Heydari
- Cumming School of Medicine, University of Calgary, Alberta, Canada
| | - Luis Zamudio
- Cedars-Sinai Medical Center, UCLA Dept. of Bioengineering, and the Laboratory for Translational Imaging of Microcirculation, Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis
| | - Rohan Dharmakumar
- Krannert Cardiovascular Research Center, Dept. of Medicine, and IU Health/IUSM Cardiovascular Institute, Indiana University School of Medicine, Indianapolis
| | - Behzad Sharif
- Cedars-Sinai Medical Center, UCLA Dept. of Bioengineering, and the Laboratory for Translational Imaging of Microcirculation, Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis
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Aramendía-Vidaurreta V, Gordaliza PM, Vidorreta M, Echeverría-Chasco R, Bastarrika G, Muñoz-Barrutia A, Fernández-Seara MA. Reduction of motion effects in myocardial arterial spin labeling. Magn Reson Med 2021; 87:1261-1275. [PMID: 34644410 DOI: 10.1002/mrm.29038] [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: 05/10/2021] [Revised: 09/09/2021] [Accepted: 09/20/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE To evaluate the accuracy and reproducibility of myocardial blood flow measurements obtained under different breathing strategies and motion correction techniques with arterial spin labeling. METHODS A prospective cardiac arterial spin labeling study was performed in 12 volunteers at 3 Tesla. Perfusion images were acquired twice under breath-hold, synchronized-breathing, and free-breathing. Motion detection based on the temporal intensity variation of a myocardial voxel, as well as image registration based on pairwise and groupwise approaches, were applied and evaluated in synthetic and in vivo data. A region of interest was drawn over the mean perfusion-weighted image for quantification. Original breath-hold datasets, analyzed with individual regions of interest for each perfusion-weighted image, were considered as reference values. RESULTS Perfusion measurements in the reference breath-hold datasets were in line with those reported in literature. In original datasets, prior to motion correction, myocardial blood flow quantification was significantly overestimated due to contamination of the myocardial perfusion with the high intensity signal of blood pool. These effects were minimized with motion detection or registration. Synthetic data showed that accuracy of the perfusion measurements was higher with the use of registration, in particular after the pairwise approach, which probed to be more robust to motion. CONCLUSION Satisfactory results were obtained for the free-breathing strategy after pairwise registration, with higher accuracy and robustness (in synthetic datasets) and higher intrasession reproducibility together with lower myocardial blood flow variability across subjects (in in vivo datasets). Breath-hold and synchronized-breathing after motion correction provided similar results, but these breathing strategies can be difficult to perform by patients.
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Affiliation(s)
- Verónica Aramendía-Vidaurreta
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.,IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Pedro M Gordaliza
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | | | - Rebeca Echeverría-Chasco
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.,IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Gorka Bastarrika
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.,IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Arrate Muñoz-Barrutia
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - María A Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.,IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
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