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Johnson LA, Harmon SA, Yilmaz EC, Lin Y, Belue MJ, Merriman KM, Lay NS, Sanford TH, Sarma KV, Arnold CW, Xu Z, Roth HR, Yang D, Tetreault J, Xu D, Patel KR, Gurram S, Wood BJ, Citrin DE, Pinto PA, Choyke PL, Turkbey B. Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods. Abdom Radiol (NY) 2024; 49:1545-1556. [PMID: 38512516 DOI: 10.1007/s00261-024-04242-7] [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: 12/17/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.
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Affiliation(s)
- Latrice A Johnson
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Karthik V Sarma
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Corey W Arnold
- Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ziyue Xu
- NVIDIA Corporation, Santa Clara, CA, USA
| | | | - Dong Yang
- NVIDIA Corporation, Santa Clara, CA, USA
| | | | - Daguang Xu
- NVIDIA Corporation, Santa Clara, CA, USA
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
- Molecular Imaging Branch (B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892, USA.
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Wu C, Montagne S, Hamzaoui D, Ayache N, Delingette H, Renard-Penna R. Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature. Insights Imaging 2022; 13:202. [PMID: 36543901 PMCID: PMC9772373 DOI: 10.1186/s13244-022-01340-2] [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: 08/11/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Accurate zonal segmentation of prostate boundaries on MRI is a critical prerequisite for automated prostate cancer detection based on PI-RADS. Many articles have been published describing deep learning methods offering great promise for fast and accurate segmentation of prostate zonal anatomy. The objective of this review was to provide a detailed analysis and comparison of applicability and efficiency of the published methods for automatic segmentation of prostate zonal anatomy by systematically reviewing the current literature. METHODS A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was conducted until June 30, 2021, using PubMed, ScienceDirect, Web of Science and EMBase databases. Risk of bias and applicability based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria adjusted with Checklist for Artificial Intelligence in Medical Imaging (CLAIM) were assessed. RESULTS A total of 458 articles were identified, and 33 were included and reviewed. Only 2 articles had a low risk of bias for all four QUADAS-2 domains. In the remaining, insufficient details about database constitution and segmentation protocol provided sources of bias (inclusion criteria, MRI acquisition, ground truth). Eighteen different types of terminology for prostate zone segmentation were found, while 4 anatomic zones are described on MRI. Only 2 authors used a blinded reading, and 4 assessed inter-observer variability. CONCLUSIONS Our review identified numerous methodological flaws and underlined biases precluding us from performing quantitative analysis for this review. This implies low robustness and low applicability in clinical practice of the evaluated methods. Actually, there is not yet consensus on quality criteria for database constitution and zonal segmentation methodology.
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Affiliation(s)
- Carine Wu
- Sorbonne Université, Paris, France.
- Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020, Paris, France.
| | - Sarah Montagne
- Sorbonne Université, Paris, France
- Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020, Paris, France
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
- GRC N° 5, Oncotype-Uro, Sorbonne Université, Paris, France
| | - Dimitri Hamzaoui
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Nicholas Ayache
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Hervé Delingette
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Raphaële Renard-Penna
- Sorbonne Université, Paris, France
- Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020, Paris, France
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
- GRC N° 5, Oncotype-Uro, Sorbonne Université, Paris, France
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Qin C, Tu P, Chen X, Troccaz J. A novel registration-based algorithm for prostate segmentation via the combination of SSM and CNN. Med Phys 2022; 49:5268-5282. [PMID: 35506596 DOI: 10.1002/mp.15698] [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: 11/14/2021] [Revised: 04/18/2022] [Accepted: 04/22/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Precise determination of target is an essential procedure in prostate interventions, such as prostate biopsy, lesion detection, and targeted therapy. However, the prostate delineation may be tough in some cases due to tissue ambiguity or lack of partial anatomical boundary. In this study, we proposed a novel supervised registration-based algorithm for precise prostate segmentation, which combine the convolutional neural network (CNN) with a statistical shape model (SSM). METHODS The proposed network mainly consists of two branches. One called SSM-Net branch was exploited to predict the shape transform matrix, shape control parameters, and shape fine-tuning vector, for the generation of the prostate boundary. Furtherly, according to the inferred boundary, a normalized distance map was calculated as the output of SSM-Net. Another branch named ResU-Net was employed to predict a probability label map from the input images at the same time. Integrating the output of these two branches, the optimal weighted sum of the distance map and the probability map was regarded as the prostate segmentation. RESULTS Two public datasets PROMISE12 and NCI-ISBI 2013 were utilized to evaluate the performance of the proposed algorithm. The results demonstrate that the segmentation algorithm achieved the best performance with an SSM of 9500 nodes, which obtained a dice of 0.907 and an average surface distance of 1.85 mm. Compared with other methods, our algorithm delineates the prostate region more accurately and efficiently. In addition, we verified the impact of model elasticity augmentation and the fine-tuning item on the network segmentation capability. As a result, both factors have improved the delineation accuracy, with dice increased by 10% and 7% respectively. CONCLUSIONS Our segmentation method has the potential to be an effective and robust approach for prostate segmentation. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Chunxia Qin
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.,School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Puxun Tu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaojun Chen
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jocelyne Troccaz
- Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC, Grenoble, France
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Montagne S, Hamzaoui D, Allera A, Ezziane M, Luzurier A, Quint R, Kalai M, Ayache N, Delingette H, Renard-Penna R. Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology. Insights Imaging 2021; 12:71. [PMID: 34089410 PMCID: PMC8179870 DOI: 10.1186/s13244-021-01010-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/05/2021] [Indexed: 12/29/2022] Open
Abstract
Background Accurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it. Methods Seven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients). Segmentation variabilities were evaluated based on: anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS ≥ 3 lesion), variation in image acquisition (3D vs 2D T2W images), and reader’s experience. Several metrics including Dice Score (DSC) and Hausdorff Distance were used to evaluate differences, with both a pairwise and a consensus (STAPLE reference) comparison. Results DSC was 0.92 (± 0.02) and 0.94 (± 0.03) for WG, 0.88 (± 0.05) and 0.91 (± 0.05) for TZ respectively with pairwise comparison and consensus reference. Variability was significantly (p < 0.05) lower for the mid-gland (DSC 0.95 (± 0.02)), higher for the apex (0.90 (± 0.06)) and the base (0.87 (± 0.06)), and higher for smaller prostates (p < 0.001) and when contrast between zones was low (p < 0.05). Impact of the other studied factors was non-significant. Conclusions Variability is higher in the extreme parts of the gland, is influenced by changes in prostate morphology (volume, zone intensity ratio), and is relatively unaffected by the radiologist’s level of expertise. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01010-9.
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Affiliation(s)
- Sarah Montagne
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France. .,Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, Paris, France. .,Sorbonne Universités, GRC n° 5, Oncotype-Uro, Paris, France.
| | - Dimitri Hamzaoui
- Inria, Epione Team, Université Côte D'Azur, Sophia Antipolis, Nice, France
| | - Alexandre Allera
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Malek Ezziane
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Anna Luzurier
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Raphaelle Quint
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Mehdi Kalai
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Nicholas Ayache
- Inria, Epione Team, Université Côte D'Azur, Sophia Antipolis, Nice, France
| | - Hervé Delingette
- Inria, Epione Team, Université Côte D'Azur, Sophia Antipolis, Nice, France
| | - Raphaële Renard-Penna
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France.,Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, Paris, France.,Sorbonne Universités, GRC n° 5, Oncotype-Uro, Paris, France
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5
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Zavala-Romero O, Breto AL, Xu IR, Chang YCC, Gautney N, Dal Pra A, Abramowitz MC, Pollack A, Stoyanova R. Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis. Strahlenther Onkol 2020; 196:932-942. [PMID: 32221622 PMCID: PMC8418872 DOI: 10.1007/s00066-020-01607-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/10/2020] [Indexed: 11/25/2022]
Abstract
PURPOSE Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors. METHODS This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2‑w. The network uses axial, coronal, and sagittal T2‑w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the PZ. Of the three, two were trained on data from each vendor separately, and a third (Combined) on the aggregate of the datasets. The Dice coefficient (DSC) is used to compare the manual and predicted segmentation. RESULTS For prostate segmentation, the Combined model obtained DSCs of 0.893 ± 0.036 and 0.825 ± 0.112 (mean ± standard deviation) on Siemens and GE, respectively. For PZ, the best DSCs were from the Combined model: 0.811 ± 0.079 and 0.788 ± 0.093. While the Siemens model underperformed on the GE dataset and vice versa, the Combined model achieved robust performance on both datasets. CONCLUSION The proposed network has a performance comparable to the interexpert variability for segmenting the prostate and its PZ. Combining images from different MRI vendors on the training of the network is of paramount importance for building a universal model for prostate and PZ segmentation.
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Affiliation(s)
- Olmo Zavala-Romero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adrian L Breto
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Isaac R Xu
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Nicole Gautney
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
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da Silva GLF, Diniz PS, Ferreira JL, França JVF, Silva AC, de Paiva AC, de Cavalcanti EAA. Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans. Med Biol Eng Comput 2020; 58:1947-1964. [DOI: 10.1007/s11517-020-02199-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 05/22/2020] [Indexed: 10/24/2022]
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7
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Kuisma A, Ranta I, Keyriläinen J, Suilamo S, Wright P, Pesola M, Warner L, Löyttyniemi E, Minn H. Validation of automated magnetic resonance image segmentation for radiation therapy planning in prostate cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2020; 13:14-20. [PMID: 33458302 PMCID: PMC7807774 DOI: 10.1016/j.phro.2020.02.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/23/2019] [Accepted: 02/24/2020] [Indexed: 01/06/2023]
Abstract
Background and purpose Magnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC. Material and methods Pelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results. Results The AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05). Conclusion Fully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use.
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Affiliation(s)
- Anna Kuisma
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
| | - Iiro Ranta
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.,University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland
| | - Jani Keyriläinen
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.,University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland
| | - Sami Suilamo
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
| | - Pauliina Wright
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
| | - Marko Pesola
- Philips MR Therapy Oy, Äyritie 4, FI-01510 Vantaa, Finland
| | - Lizette Warner
- Philips MR Oncology, 3000 Minuteman Road, Andover, MA 01810, United States
| | - Eliisa Löyttyniemi
- University of Turku, Department of Biostatistics, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
| | - Heikki Minn
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
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Comelli A, Stefano A, Coronnello C, Russo G, Vernuccio F, Cannella R, Salvaggio G, Lagalla R, Barone S. Radiomics: A New Biomedical Workflow to Create a Predictive Model. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2020. [DOI: 10.1007/978-3-030-52791-4_22] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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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: 122] [Impact Index Per Article: 24.4] [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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10
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Shahedi M, Halicek M, Li Q, Liu L, Zhang Z, Verma S, Schuster DM, Fei B. A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10951:109512I. [PMID: 32528212 PMCID: PMC7289512 DOI: 10.1117/12.2512282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts' manual reference segmentations. The measured mean ± standard deviation of error values for the whole gland were 1.4 ± 0.4 mm, 8.5 ± 2.0 mm, and 86 ± 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5 mm (MAD), 9.0 mm (HDist), and 83% (DSC). The proposed algorithm illustrated a fast, accurate, and robust performance for 3D prostate segmentation. The accuracy of the algorithm is within the inter-expert variability observed in manual segmentation and comparable to the best performance results reported in the literature.
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Affiliation(s)
- Maysam Shahedi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - Martin Halicek
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA
| | - Qinmei Li
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Radiology, The Second Affiliated Hospital of Guangzhou, Medical University, Guangzhou, China
| | - Lizhi Liu
- State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zhenfeng Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou, Medical University, Guangzhou, China
| | - Sadhna Verma
- Department of Radiology, University of Cincinnati Medical Center and The Veterans Administration Hospital, Cincinnati, OH
| | - David M. Schuster
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
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Luo Y, Xu Y, Liao Z, Gomez D, Wang J, Jiang W, Zhou R, Williamson R, Court LE, Yang J. Automatic segmentation of cardiac substructures from noncontrast CT images: accurate enough for dosimetric analysis? Acta Oncol 2019; 58:81-87. [PMID: 30306817 DOI: 10.1080/0284186x.2018.1521985] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE We evaluated the feasibility of using an automatic segmentation tool to delineate cardiac substructures from noncontrast computed tomography (CT) images for cardiac dosimetry and toxicity analyses for patients with nonsmall cell lung cancer (NSCLC) after radiotherapy. MATERIAL AND METHODS We used an in-house developed multi-atlas segmentation tool to delineate 11cardiac substructures, including the whole heart, four heart chambers, and six greater vessels, automatically from the averaged 4D-CT planning images of 49 patients with NSCLC. Two experienced radiation oncologists edited the auto-segmented contours. Times for automatic segmentation and modification were recorded. The modified contours were compared with the auto-segmented contours in terms of Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate the extent of modification. Differences in dose-volume histogram (DVH) characteristics were also evaluated for the modified versus auto-segmented contours. RESULTS The mean automatic segmentation time for all 11 structures was 7-9 min. For the 49 patients, the mean DSC values (±SD) ranged from .73 ± .08 to .95 ± .04, and the mean MSD values ranged from 1.3 ± .6 mm to 2.9 ± 5.1 mm. Overall, the modifications were small; the largest modifications were in the pulmonary vein and the inferior vena cava. The heart V30 (volume receiving dose ≥30 Gy) and the mean dose to the whole heart and the four heart chambers were not different for the modified versus the auto-segmented contours based on the statistically significant condition of p < .05. Also, the maximum dose to the great vessels was no different except for the pulmonary vein. CONCLUSIONS Automatic segmentation of cardiac substructures did not require substantial modifications. Dosimetric evaluation showed no significant difference between the auto-segmented and modified contours for most structures, which suggests that the auto-segmented contours can be used to study cardiac dose-responses in clinical practice.
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Affiliation(s)
- Yangkun Luo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Oncology, Sichuan Cancer Hospital, Chengdu, China
| | - Yujin Xu
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Daniel Gomez
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jingqian Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Jiang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rongrong Zhou
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Ryan Williamson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Towards a universal MRI atlas of the prostate and prostate zones : Comparison of MRI vendor and image acquisition parameters. Strahlenther Onkol 2018; 195:121-130. [PMID: 30140944 DOI: 10.1007/s00066-018-1348-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 07/31/2018] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND PURPOSE The aim of this study was to evaluate an automatic multi-atlas-based segmentation method for generating prostate, peripheral (PZ), and transition zone (TZ) contours on MRIs with and without fat saturation (±FS), and compare MRIs from different vendor MRI systems. METHODS T2-weighted (T2) and fat-saturated (T2FS) MRIs were acquired on 3T GE (GE, Waukesha, WI, USA) and Siemens (Erlangen, Germany) systems. Manual prostate and PZ contours were used to create atlas libraries. As a test MRI is entered, the procedure for atlas segmentation automatically identifies the atlas subjects that best match the test subject, followed by a normalized intensity-based free-form deformable registration. The contours are transformed to the test subject, and Dice similarity coefficients (DSC) and Hausdorff distances between atlas-generated and manual contours were used to assess performance. RESULTS Three atlases were generated based on GE_T2 (n = 30), GE_T2FS (n = 30), and Siem_T2FS (n = 31). When test images matched the contrast and vendor of the atlas, DSCs of 0.81 and 0.83 for T2 ± FS were obtained (baseline performance). Atlases performed with higher accuracy when segmenting (i) T2FS vs. T2 images, likely due to a superior contrast between prostate vs. surrounding tissue; (ii) prostate vs. zonal anatomy; (iii) in the mid-gland vs. base and apex. Atlases performance declined when tested with images with differing contrast and MRI vendor. Conversely, combined atlases showed similar performance to baseline. CONCLUSION The MRI atlas-based segmentation method achieved good results for prostate, PZ, and TZ compared to expert contoured volumes. Combined atlases performed similarly to matching atlas and scan type. The technique is fast, fully automatic, and implemented on commercially available clinical platform.
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Dill V, Klein PC, Franco AR, Pinho MS. Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters. Comput Biol Med 2018; 95:90-98. [DOI: 10.1016/j.compbiomed.2018.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 02/07/2018] [Accepted: 02/08/2018] [Indexed: 10/18/2022]
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Cheplygina V, Pena IP, Pedersen JH, Lynch DA, Sorensen L, de Bruijne M. Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform 2017; 22:1486-1496. [PMID: 29990220 DOI: 10.1109/jbhi.2017.2769800] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a lung disease that can be quantified using chest computed tomography scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multicenter dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multicenter classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains can further improve the results. To encourage further research into transfer learning methods for the classification of COPD, upon acceptance of this paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd.
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Cheng R, Roth HR, Lay N, Lu L, Turkbey B, Gandler W, McCreedy ES, Pohida T, Pinto PA, Choyke P, McAuliffe MJ, Summers RM. Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J Med Imaging (Bellingham) 2017; 4:041302. [PMID: 28840173 DOI: 10.1117/1.jmi.4.4.041302] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 05/22/2017] [Indexed: 11/14/2022] Open
Abstract
Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of [Formula: see text] and a mean Jaccard similarity coefficient (IoU) of [Formula: see text] are used to calculate without trimming any end slices. The proposed holistic model significantly ([Formula: see text]) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature.
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Affiliation(s)
- Ruida Cheng
- Imaging Sciences Laboratory, Center of Information Technology, NIH, Bethesda, Maryland, United States
| | - Holger R Roth
- Imaging Biomarkers and CAD Laboratory, Clinical Center, NIH, Bethesda, Maryland, United States
| | - Nathan Lay
- Imaging Biomarkers and CAD Laboratory, Clinical Center, NIH, Bethesda, Maryland, United States
| | - Le Lu
- Imaging Biomarkers and CAD Laboratory, Clinical Center, NIH, Bethesda, Maryland, United States
| | - Baris Turkbey
- Molecular Imaging Program, NCI, Bethesda, Maryland, United States
| | - William Gandler
- Imaging Sciences Laboratory, Center of Information Technology, NIH, Bethesda, Maryland, United States
| | - Evan S McCreedy
- Imaging Sciences Laboratory, Center of Information Technology, NIH, Bethesda, Maryland, United States
| | - Tom Pohida
- Computational Bioscience and Engineering Laboratory, Center of Information Technology, NIH, Bethesda, Maryland, United States
| | - Peter A Pinto
- Center of Cancer Research, Urologic Oncology Branch, Bethesda, Maryland, United States
| | - Peter Choyke
- Molecular Imaging Program, NCI, Bethesda, Maryland, United States
| | - Matthew J McAuliffe
- Imaging Sciences Laboratory, Center of Information Technology, NIH, Bethesda, Maryland, United States
| | - Ronald M Summers
- Imaging Biomarkers and CAD Laboratory, Clinical Center, NIH, Bethesda, Maryland, United States
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Khadra M. Automatic prostate segmentation on MR images with deep network and graph model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:635-638. [PMID: 28268408 DOI: 10.1109/embc.2016.7590782] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automated prostate diagnoses and treatments have gained much attention due to the high mortality rate of prostate cancer. In particular, unsupervised (automatic) prostate segmentation is an active and challenging research. Most conventional works usually utilize handcrafted (low-level) features for prostate segmentation; however they often fail to extract the intrinsic structure of the prostate, especially on images with blurred boundaries. In this paper, we propose a novel automated prostate segmentation model with learned features from deep network. Specifically, we first generate a set of prostate proposals in transverse plane via recognizing the position and coarse estimate of the shape of the prostate on the global prostate image and using the deep network to extract highly effective features for the boundary refinement in a finer scale. With consideration of the correlations among different sequential images, we then construct a graph to select the best prostate proposals from proposal set for its use in 3D prostate segmentation. Experimental evaluation demonstrates that our proposed deep network and graph based method is superior to state-of-the-art couterparts, in terms of both dice similarity coefficient and Hausdorff distance, on public dataset.
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Jiang XL, Wang Q, He B, Chen SJ, Li BL. Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.046] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Irreversible Electroporation of Prostate Cancer: Patient-Specific Pretreatment Simulation by Electric Field Measurement in a 3D Bioprinted Textured Prostate Cancer Model to Achieve Optimal Electroporation Parameters for Image-Guided Focal Ablation. Cardiovasc Intervent Radiol 2016; 39:1668-1671. [DOI: 10.1007/s00270-016-1390-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 05/23/2016] [Indexed: 12/17/2022]
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Tian Z, Liu L, Zhang Z, Fei B. Superpixel-Based Segmentation for 3D Prostate MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:791-801. [PMID: 26540678 PMCID: PMC4831070 DOI: 10.1109/tmi.2015.2496296] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
This paper proposes a method for segmenting the prostate on magnetic resonance (MR) images. A superpixel-based 3D graph cut algorithm is proposed to obtain the prostate surface. Instead of pixels, superpixels are considered as the basic processing units to construct a 3D superpixel-based graph. The superpixels are labeled as the prostate or background by minimizing an energy function using graph cut based on the 3D superpixel-based graph. To construct the energy function, we proposed a superpixel-based shape data term, an appearance data term, and two superpixel-based smoothness terms. The proposed superpixel-based terms provide the effectiveness and robustness for the segmentation of the prostate. The segmentation result of graph cuts is used as an initialization of a 3D active contour model to overcome the drawback of the graph cut. The result of 3D active contour model is then used to update the shape model and appearance model of the graph cut. Iterations of the 3D graph cut and 3D active contour model have the ability to jump out of local minima and obtain a smooth prostate surface. On our 43 MR volumes, the proposed method yields a mean Dice ratio of 89.3 ±1.9%. On PROMISE12 test data set, our method was ranked at the second place; the mean Dice ratio and standard deviation is 87.0±3.2%. The experimental results show that the proposed method outperforms several state-of-the-art prostate MRI segmentation methods.
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Affiliation(s)
- Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30329 USA
| | - Lizhi Liu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30329 USA. Center for Medical Imaging & Image-guided Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zhenfeng Zhang
- Center for Medical Imaging & Image-guided Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, also with Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30329 USA. website: www.feilab.org
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