1
|
Filizoglu N, Ozguven S, Akin Telli T, Ones T, Dede F, Turoglu HT, Erdil TY. Defining the optimal segmentation method for measuring somatostatin receptor expressing tumor volume on 68 Ga-DOTATATE positron emission tomography/computed tomography to predict prognosis in patients with gastroenteropancreatic neuroendocrine tumors. Nucl Med Commun 2024; 45:736-744. [PMID: 38745508 DOI: 10.1097/mnm.0000000000001861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
OBJECTIVE We aimed to compare different segmentation methods used to calculate prognostically valuable volumetric parameters, somatostatin receptor expressing tumor volume (SRETV), and total lesion somatostatin receptor expression (TLSRE), measured by 68 Ga-DOTATATE PET/CT and to find the optimal segmentation method to predict prognosis. PATIENTS AND METHODS Images of 34 patients diagnosed with gastroenteropancreatic neuroendocrine tumor (GEPNET) who underwent 68 Ga-DOTATATE PET/CT imaging were reanalyzed. Four different threshold-based methods (fixed relative threshold method, normal liver background threshold method, fixed absolute standardized uptake value (SUV) threshold method, and adaptive threshold method) were used to calculate SRETV and TLSRE values. SRETV of all lesions of a patient was summarized as whole body SRETV (WB-SRETV) and TLSRE of all lesions of a patient was computed as whole body TLSRE (WB-TLSRE). RESULTS WB-SRETVs calculated with all segmentation methods were statistically significantly associated with progression-free survival except WB-SRETV at which was calculated using adaptive threshold method. The fixed relative threshold methods calculated by using 45% (WB-SRETV 45% ) and 60% (WB-SRETV 60% ) of the SUV value as threshold respectively, were found to have statistically significant highest prognostic value (C-index = 0.704, CI = 0.622-0.786, P = 0.007). Among WB-TLSRE parameters, WB-TLSRE 35% , WB-TLSRE 40% , and WB-TLSRE 50% had the highest prognostic value (C-index = 0.689, CI = 0.604-0.774, P = 0.008). CONCLUSION The fixed relative threshold method was found to be the most effective and easily applicable method to measure SRETV on pretreatment 68 Ga-DOTATATE PET/CT to predict prognosis in GEPNET patients. WB-SRETV 45% (cutoff value of 11.8 cm 3 ) and WB-SRETV 60% (cutoff value of 6.3 cm 3 ) were found to be the strongest predictors of prognosis in GEPNET patients.
Collapse
Affiliation(s)
- Nuh Filizoglu
- Department of Nuclear Medicine, University of Health Sciences, Kartal Dr. Lutfi Kirdar City Hospital,
| | - Salih Ozguven
- Department of Nuclear Medicine, Marmara University Pendik Training and Research Hospital and
| | - Tugba Akin Telli
- Department of Oncology, Memorial Sisli Hospital, Istanbul, Turkey
| | - Tunc Ones
- Department of Nuclear Medicine, Marmara University Pendik Training and Research Hospital and
| | - Fuat Dede
- Department of Nuclear Medicine, Marmara University Pendik Training and Research Hospital and
| | - Halil T Turoglu
- Department of Nuclear Medicine, Marmara University Pendik Training and Research Hospital and
| | - Tanju Y Erdil
- Department of Nuclear Medicine, Marmara University Pendik Training and Research Hospital and
| |
Collapse
|
2
|
Zhang W, Ray S. From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images. FRONTIERS IN RADIOLOGY 2023; 3:1225215. [PMID: 37745205 PMCID: PMC10512384 DOI: 10.3389/fradi.2023.1225215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023]
Abstract
With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, such as intensity modulated radiation therapy (IMRT) dose painting, demand the precise delineation of multiple intensity value contours to ensure optimal tumour dose distribution. Recently, convolutional neural networks (CNNs) have made significant strides in 3D image segmentation tasks, most of which present the output map at a voxel-wise level. However, because of information loss in subsequent downsampling layers, they frequently fail to precisely identify precise object boundaries. Moreover, in the context of dose painting strategies, there is an imperative need for reliable and precise image segmentation techniques to delineate high recurrence-risk contours. To address these challenges, we introduce a 3D coarse-to-fine framework, integrating a CNN with a kernel smoothing-based probability volume contour approach (KsPC). This integrated approach generates contour-based segmentation volumes, mimicking expert-level precision and providing accurate probability contours crucial for optimizing dose painting/IMRT strategies. Our final model, named KsPC-Net, leverages a CNN backbone to automatically learn parameters in the kernel smoothing process, thereby obviating the need for user-supplied tuning parameters. The 3D KsPC-Net exploits the strength of KsPC to simultaneously identify object boundaries and generate corresponding probability volume contours, which can be trained within an end-to-end framework. The proposed model has demonstrated promising performance, surpassing state-of-the-art models when tested against the MICCAI 2021 challenge dataset (HECKTOR).
Collapse
Affiliation(s)
- Wenhui Zhang
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | | |
Collapse
|
3
|
Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J, Vallières M, Zhu S, Xie J, Peng Y, Iantsen A, Hatt M, Yuan Y, Ma J, Yang X, Rao C, Pai S, Ghimire K, Feng X, Naser MA, Fuller CD, Yousefirizi F, Rahmim A, Chen H, Wang L, Prior JO, Depeursinge A. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Med Image Anal 2021; 77:102336. [PMID: 35016077 DOI: 10.1016/j.media.2021.102336] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/13/2021] [Accepted: 12/14/2021] [Indexed: 12/23/2022]
Abstract
This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs.
Collapse
Affiliation(s)
- Valentin Oreiller
- Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
| | - Vincent Andrearczyk
- Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Hesham Elhalawani
- Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Joel Castelli
- Radiotherapy Department, Cancer Institute Eugène Marquis, Rennes, France
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Simeng Zhu
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, PR China
| | - Ying Peng
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, PR China
| | - Andrei Iantsen
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jun Ma
- Department of Mathematics, Nanjing University of Science and Technology, Jiangsu, China
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Jiangsu, China
| | - Chinmay Rao
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Suraj Pai
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Xue Feng
- Carina Medical, Lexington, KY, 40513, USA; Department of Biomedical Engineering, University of Virginia, Charlottesville VA 22903, USA
| | - Mohamed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver BC, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver BC, Canada
| | - Huai Chen
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shangai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Lisheng Wang
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shangai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Adrien Depeursinge
- Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| |
Collapse
|
4
|
Pellegrino S, Fonti R, Pulcrano A, Del Vecchio S. PET-Based Volumetric Biomarkers for Risk Stratification of Non-Small Cell Lung Cancer Patients. Diagnostics (Basel) 2021; 11:diagnostics11020210. [PMID: 33573333 PMCID: PMC7911597 DOI: 10.3390/diagnostics11020210] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 12/26/2022] Open
Abstract
Despite the recent advances in lung cancer biology, molecular pathology, and treatment, this malignancy remains the leading cause of cancer-related death worldwide and non-small cell lung cancer (NSCLC) is the most common form found at diagnosis. Accurate staging of the disease is a fundamental prognostic factor that correctly predicts progression-free (PFS) and overall survival (OS) of NSCLC patients. However, outcome of patients within each TNM staging group can change widely highlighting the need to identify additional prognostic biomarkers to better stratify patients on the basis of risk. 18F-FDG PET/CT plays an essential role in staging, evaluation of treatment response, and tumoral target delineation in NSCLC patients. Moreover, a number of studies showed the prognostic role of imaging parameters derived from PET images, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG). These parameters represent three-dimensional PET-based measurements providing information on both tumor volume and metabolic activity and previous studies reported their ability to predict OS and PFS of NSCLC patients. This review will primarily focus on the studies that showed the prognostic and predictive role of MTV and TLG in NSCLC patients, addressing also their potential utility in the new era of immunotherapy of NSCLC.
Collapse
Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University “Federico II”, 80131 Naples, Italy; (S.P.); (A.P.)
| | - Rosa Fonti
- Institute of Biostructures and Bioimages, National Research Council, 80145 Naples, Italy;
| | - Alessandro Pulcrano
- Department of Advanced Biomedical Sciences, University “Federico II”, 80131 Naples, Italy; (S.P.); (A.P.)
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University “Federico II”, 80131 Naples, Italy; (S.P.); (A.P.)
- Correspondence: ; Tel.: +39-081-7463307; Fax: +39-081-5457081
| |
Collapse
|
5
|
Lian C, Ruan S, Denoeux T, Li H, Vera P. Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:755-766. [PMID: 30296224 PMCID: PMC8191586 DOI: 10.1109/tip.2018.2872908] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Precise delineation of target tumor is a key factor to ensure the effectiveness of radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practice of radiation oncology, many existing automatic/semi-automatic methods still perform tumor segmentation on mono-modal images. In this paper, a co-clustering algorithm is proposed to concurrently segment 3D tumors in PET-CT images, considering that the two complementary imaging modalities can combine functional and anatomical information to improve segmentation performance. The theory of belief functions is adopted in the proposed method to model, fuse, and reason with uncertain and imprecise knowledge from noisy and blurry PET-CT images. To ensure reliable segmentation for each modality, the distance metric for the quantification of clustering distortions and spatial smoothness is iteratively adapted during the clustering procedure. On the other hand, to encourage consistent segmentation between different modalities, a specific context term is proposed in the clustering objective function. Moreover, during the iterative optimization process, clustering results for the two distinct modalities are further adjusted via a belief-functions-based information fusion strategy. The proposed method has been evaluated on a data set consisting of 21 paired PET-CT images for non-small cell lung cancer patients. The quantitative and qualitative evaluations show that our proposed method performs well compared with the state-of-the-art methods.
Collapse
|
6
|
Taghanaki SA, Duggan N, Ma H, Hou X, Celler A, Benard F, Hamarneh G. Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Comput Med Imaging Graph 2018; 63:52-66. [DOI: 10.1016/j.compmedimag.2017.12.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 11/16/2017] [Accepted: 12/20/2017] [Indexed: 11/29/2022]
|
7
|
The first MICCAI challenge on PET tumor segmentation. Med Image Anal 2017; 44:177-195. [PMID: 29268169 DOI: 10.1016/j.media.2017.12.007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 12/07/2017] [Accepted: 12/07/2017] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge. MATERIALS AND METHODS Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value. RESULTS Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods. CONCLUSION The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.
Collapse
|
8
|
Im HJ, Bradshaw T, Solaiyappan M, Cho SY. Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? Nucl Med Mol Imaging 2017; 52:5-15. [PMID: 29391907 DOI: 10.1007/s13139-017-0493-6] [Citation(s) in RCA: 154] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 08/17/2017] [Accepted: 08/28/2017] [Indexed: 12/22/2022] Open
Abstract
Numerous methods to segment tumors using 18F-fluorodeoxyglucose positron emission tomography (FDG PET) have been introduced. Metabolic tumor volume (MTV) refers to the metabolically active volume of the tumor segmented using FDG PET, and has been shown to be useful in predicting patient outcome and in assessing treatment response. Also, tumor segmentation using FDG PET has useful applications in radiotherapy treatment planning. Despite extensive research on MTV showing promising results, MTV is not used in standard clinical practice yet, mainly because there is no consensus on the optimal method to segment tumors in FDG PET images. In this review, we discuss currently available methods to measure MTV using FDG PET, and assess the advantages and disadvantages of the methods.
Collapse
Affiliation(s)
- Hyung-Jun Im
- 1Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI USA.,2Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Tyler Bradshaw
- 1Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI USA
| | - Meiyappan Solaiyappan
- 3Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Steve Y Cho
- 1Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI USA.,3Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA.,4University of Wisconsin Carbone Cancer Center, Madison, WI USA
| |
Collapse
|
9
|
Hatt M, Lee JA, Schmidtlein CR, Naqa IE, Caldwell C, De Bernardi E, Lu W, Das S, Geets X, Gregoire V, Jeraj R, MacManus MP, Mawlawi OR, Nestle U, Pugachev AB, Schöder H, Shepherd T, Spezi E, Visvikis D, Zaidi H, Kirov AS. Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211. Med Phys 2017; 44:e1-e42. [PMID: 28120467 DOI: 10.1002/mp.12124] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 12/09/2016] [Accepted: 01/04/2017] [Indexed: 12/14/2022] Open
Abstract
PURPOSE The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. APPROACH A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. FINDINGS A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. CONCLUSIONS Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.
Collapse
Affiliation(s)
- Mathieu Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, IBSAM, Brest, France
| | - John A Lee
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | | | | | - Curtis Caldwell
- Sunnybrook Health Sciences Center, Toronto, ON, M4N 3M5, Canada
| | | | - Wei Lu
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shiva Das
- University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Xavier Geets
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | - Vincent Gregoire
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | - Robert Jeraj
- University of Wisconsin, Madison, WI, 53705, USA
| | | | | | - Ursula Nestle
- Universitätsklinikum Freiburg, Freiburg, 79106, Germany
| | - Andrei B Pugachev
- University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Heiko Schöder
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | | | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, United Kingdom
| | | | - Habib Zaidi
- Geneva University Hospital, Geneva, CH-1211, Switzerland
| | - Assen S Kirov
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| |
Collapse
|
10
|
Giri MG, Cavedon C, Mazzarotto R, Ferdeghini M. A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions. Med Phys 2017; 43:2491. [PMID: 27147360 DOI: 10.1118/1.4947123] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
PURPOSE The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. METHODS The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10-37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. RESULTS Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to automatically generate the optimal parameter from the variance of the ROI. This "calibration curve" was then applied to contour the whole data set. The accuracy (mean discrepancy between DPM model-based contours and reference contours) of volume estimation was below (1 ± 7)% on the whole data set (1 SD). The overlap between true and automatically segmented contours, measured by the Dice similarity coefficient, was 0.93 with a SD of 0.03. CONCLUSIONS The proposed DPM model was able to accurately reproduce known volumes of FDG concentration, with high overlap between segmented and true volumes. For all the analyzed inserts of the IEC phantom, the algorithm proved to be robust to variations in radius and in TBR. The main advantage of this algorithm was that no setting of DPM parameters was required in advance, since the proper setting of the only parameter that could significantly influence the segmentation results was automatically related to the uptake variance of the chosen ROI. Furthermore, the algorithm did not need any preliminary choice of the optimum number of classes to describe the ROIs within PET images and no assumption about the shape of the lesion and the uptake heterogeneity of the tracer was required.
Collapse
Affiliation(s)
- Maria Grazia Giri
- Medical Physics Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126, Italy
| | - Carlo Cavedon
- Medical Physics Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126, Italy
| | - Renzo Mazzarotto
- Radiation Oncology Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126, Italy
| | - Marco Ferdeghini
- Nuclear Medicine Unit, University Hospital of Verona, P.le Stefani 1, Verona 37126, Italy
| |
Collapse
|
11
|
Zhuang M, Dierckx RAJO, Zaidi H. Generic and robust method for automatic segmentation of PET images using an active contour model. Med Phys 2016; 43:4483. [PMID: 27487865 DOI: 10.1118/1.4954844] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. METHODS MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer's thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. RESULTS For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (-8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of -13.35% ± 11.87% and -11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. CONCLUSIONS The authors' results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors' study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.
Collapse
Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; Department of Radiation Oncology, Tumor Hospital of Shantou University Medical College, Shantou, Guangdong 515000, China; and The Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, Guangdong 515000, China
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland; and Department of Nuclear Medicine and Molecular Imaging,University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| |
Collapse
|