1
|
Taiello R, Önen M, Capano F, Humbert O, Lorenzi M. Privacy preserving image registration. Med Image Anal 2024; 94:103129. [PMID: 38471338 DOI: 10.1016/j.media.2024.103129] [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: 03/23/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we propose several techniques to optimize the image registration operations by using gradient approximations, and by revisiting the use of homomorphic encryption trough packing, to allow the efficient encryption and multiplication of large matrices. We focus on registration methods of increasing complexity, including rigid, affine, and non-linear registration based on cubic splines or diffeomorphisms parameterized by time-varying velocity fields. In all these settings, we demonstrate how the registration problem can be naturally adapted for accounting to privacy-preserving operations, and illustrate the effectiveness of PPIR on a variety of registration tasks.
Collapse
Affiliation(s)
- Riccardo Taiello
- Epione Research Group, Inria, Sophia Antipolis, France; EURECOM, France; Université Côte d'Azur, France.
| | | | | | | | - Marco Lorenzi
- Epione Research Group, Inria, Sophia Antipolis, France; Université Côte d'Azur, France
| |
Collapse
|
2
|
Du W, Yin K, Shi J. Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging. Brain Sci 2023; 13:1549. [PMID: 38002509 PMCID: PMC10669566 DOI: 10.3390/brainsci13111549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.
Collapse
Affiliation(s)
- Wentao Du
- Nanjing Research Institute of Electronic Technology, Nanjing 210019, China;
| | - Kuiying Yin
- Nanjing Research Institute of Electronic Technology, Nanjing 210019, China;
| | - Jingping Shi
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China;
| |
Collapse
|
3
|
Schmithorst V, Ceschin R, Lee V, Wallace J, Sahel A, Chenevert TL, Parmar H, Berman JI, Vossough A, Qiu D, Kadom N, Grant PE, Gagoski B, LaViolette PS, Maheshwari M, Sleeper LA, Bellinger DC, Ilardi D, O’Neil S, Miller TA, Detterich J, Hill KD, Atz AM, Richmond ME, Cnota J, Mahle WT, Ghanayem NS, Gaynor JW, Goldberg CS, Newburger JW, Panigrahy A. Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study. Diagnostics (Basel) 2023; 13:1604. [PMID: 37174995 PMCID: PMC10178603 DOI: 10.3390/diagnostics13091604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred that were related to difficulties with: (1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and (2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by: (1) adding additional study sites, (2) increasing the frequency of meetings with site coordinators, and (3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms.
Collapse
Affiliation(s)
- Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
- Department of Biomedical Informatics, University of Pittsburgh School, 5607 Baum Blvd., Pittsburgh, PA 15206, USA
| | - Vincent Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Aurelia Sahel
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Thomas L. Chenevert
- Michigan Medicine Department of Radiology, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Hemant Parmar
- Michigan Medicine Department of Radiology, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Arastoo Vossough
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322, USA
| | - Nadja Kadom
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322, USA
| | - Patricia Ellen Grant
- Children’s Hospital Boston, Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC), 300 Longwood Avenue, Boston, MA 02115, USA
| | - Borjan Gagoski
- Department of Radiology, Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Mohit Maheshwari
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - David C. Bellinger
- Cardiac Neurodevelopmental Program, Department of Neurology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Dawn Ilardi
- Department of Neuropsychology, Children’s Healthcare of Atlanta, 1400 Tullie Road NE, Atlanta, GA 30329, USA
| | - Sharon O’Neil
- Children’s Hospital Los Angeles, Neuropsychology Core of the Saban Research Institute, 4661 Sunset Blvd., Los Angeles, CA 90027, USA
| | - Thomas A. Miller
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah School of Medicine, 30 N 1900 E, Salt Lake City, UT 84132, USA
| | - Jon Detterich
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd., Los Angeles, CA 90027, USA
| | - Kevin D. Hill
- Division of Pediatric Cardiology, Department of Pediatrics, Duke University School of Medicine, 7506 Hospital North, DUMC Box 3090, Durham, NC 27710, USA
| | - Andrew M. Atz
- Division of Pediatric Cardiology, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425, USA
| | - Marc E. Richmond
- Program for Pediatric Cardiomyopathy, Heart Failure, and Transplantation, New York-Presbyterian Morgan Stanley Children’s Hospital, 3959 Broadway MSCH North, 2nd Floor, New York, NY 10032, USA
| | - James Cnota
- Fetal Heart Program, Cincinnati Children’s, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - William T. Mahle
- Division of Pediatric Cardiology, Children’s Healthcare of Atlanta, 1400 Tullie Rd NE Suite 630, Atlanta, GA 30329, USA
| | - Nancy S. Ghanayem
- Section of Pediatric Critical Care, Department of Pediatrics, Comer Children’s Hospital, University of Chicago Medicine, 5721 S. Maryland Avenue, Chicago, IL 60637, USA
- Department of Pediatrics, Medical College of Wisconsin Section of Pediatric Critical Care, 9000 W. Wisconsin Avenue MS 681, Milwaukee, WI 53226, USA
| | - J. William Gaynor
- Heart Failure and Transplant Program, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Caren S. Goldberg
- Department of Pediatrics, Division of Cardiology, C.S. Mott Children’s Hospital, 1540 E Hospital Dr #4204, Ann Arbor, MI 48109, USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| |
Collapse
|
4
|
Schmithorst V, Ceschin R, Lee V, Wallace J, Sahel A, Chenevert T, Parmar H, Berman JI, Vossough A, Qiu D, Kadom N, Grant PE, Gagoski B, LaViolette P, Maheshwari M, Sleeper LA, Bellinger D, Ilardi D, O’Neil S, Miller TA, Detterich J, Hill KD, Atz AM, Richmond M, Cnota J, Mahle WT, Ghanayem N, Gaynor W, Goldberg CS, Newburger JW, Panigrahy A. Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.12.23288433. [PMID: 37131744 PMCID: PMC10153324 DOI: 10.1101/2023.04.12.23288433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred related to difficulties with: 1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and 2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by 1) adding additional study sites, 2) increasing the frequency of meetings with site coordinators and 3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms. Trial registration number ClinicalTrials.gov Registration Number: NCT02692443.
Collapse
Affiliation(s)
- Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
- Department of Biomedical Informatics, University of Pittsburgh School, 5607 Baum Blvd, Pittsburgh, PA 15206-3701 USA
| | - Vince Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Aurelia Sahel
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Thomas Chenevert
- Department of Radiology, Michigan Medicine, University of Michigan, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109 USA
| | - Hemant Parmar
- Department of Radiology, Michigan Medicine, University of Michigan, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109 USA
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Arastoo Vossough
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 USA
| | - Nadja Kadom
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 USA
| | - Patricia Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 USA
| | - Borjan Gagoski
- Department of Radiology, Children’s Hospital Boston, 300 Longwood Ave, Boston, MA 02115 USA
| | - Peter LaViolette
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226 USA
| | - Mohit Maheshwari
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226 USA
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
- Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115 USA
| | - David Bellinger
- Cardiac Neurodevelopmental Program, Department of Neurology, Boston, Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115 USA
| | - Dawn Ilardi
- Department of Neuropsychology, Children’s Healthcare of Atlanta, 1400 Tullie Road NE, Atlanta, GA 30329
| | - Sharon O’Neil
- Neuropsychology Core of the Saban Research Institute, Children’s Hospital Los Angeles, 4661 Sunset Blvd., Los Angeles, CA 90027 USA
| | - Thomas A. Miller
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah, School of Medicine, 30 N 1900 E, Salt Lake City, UT 84132 USA
| | - Jon Detterich
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd, Los Angeles, CA 90027 USA
| | - Kevin D. Hill
- Division of Pediatric Cardiology, Department of Pediatrics, Duke University, School of Medicine, 7506 Hospital North, DUMC Box 3090, Durham, NC 27710 USA
| | - Andrew M. Atz
- Division of Pediatric Cardiology, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425 USA
| | - Marc Richmond
- Program for Pediatric Cardiomyopathy, Heart Failure, and Transplantation, New York-Presbyterian Morgan Stanley Children’s Hospital, 3959 Broadway MSCH North, 2 Floor, New York, NY 10032 USA
| | - James Cnota
- Fetal Heart Program, Cincinnati Children’s, 3333 Burnet Avenue, Cincinnati, Ohio 45229-3026 USA
| | - William T. Mahle
- Division of Pediatric Cardiology, Children’s Healthcare of Atlanta, 1400 Tullie Rd NE Suite 630, Atlanta, GA 30329
| | - Nancy Ghanayem
- Section of Pediatric Critical Care, Department of Pediatrics, University of Chicago Medicine, Comer Children’s Hospital, 5721 S. Maryland Ave., Chicago, IL 60637 USA
- Section of Pediatric Critical Care, Department of Pediatrics, Medical College of Wisconsin, 9000 W. Wisconsin Ave. MS 681, Milwaukee, WI 53226 USA
| | - William Gaynor
- Heart Failure and Transplant Program, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Caren S. Goldberg
- Department of Pediatrics, Division of Cardiology, C.S. Mott Children’s Hospital, 1540 E Hospital Dr #4204, Ann Arbor, MI 48109 USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
| | - Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| |
Collapse
|
5
|
Praveenkumar S, Kalaiselvi T, Somasundaram K. Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review. Crit Rev Biomed Eng 2023; 51:1-40. [PMID: 37581349 DOI: 10.1615/critrevbiomedeng.2023047606] [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: 08/16/2023]
Abstract
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.
Collapse
Affiliation(s)
| | - T Kalaiselvi
- Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram 624302, Tamil Nadu, India
| | | |
Collapse
|
6
|
Ma X, Zhao Y, Lu Y, Li P, Li X, Mei N, Wang J, Geng D, Zhao L, Yin B. A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images. Comput Biol Med 2022; 151:106279. [PMID: 36375416 DOI: 10.1016/j.compbiomed.2022.106279] [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: 07/05/2022] [Revised: 10/11/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND OBJECTIVE Treatment for meningiomas usually includes surgical removal, radiation therapy, and chemotherapy. Accurate segmentation of tumors significantly facilitates complete surgical resection and precise radiotherapy, thereby improving patient survival. In this paper, a deep learning model is constructed for magnetic resonance T1-weighted Contrast Enhancement (T1CE) images to develop an automatic processing scheme for accurate tumor segmentation. METHODS In this paper, a novel Convolutional Neural Network (CNN) model is proposed for the accurate meningioma segmentation in MR images. It can extract fused features in multi-scale receptive fields of the same feature map based on MR image characteristics of meningiomas. The attention mechanism is added as a helpful addition to the model to optimize the feature information transmission. RESULTS AND CONCLUSIONS The results were evaluated on two internal testing sets and one external testing set. Mean Dice Similarity Coefficient (DSC) values of 0.886, 0.851, and 0.874 are demonstrated, respectively. In this paper, a deep learning approach is proposed to segment tumors in T1CE images. Multi-center testing sets validated the effectiveness and generalization of the method. The proposed model demonstrates state-of-the-art tumor segmentation performance.
Collapse
Affiliation(s)
- Xin Ma
- The School of Engineering and Technology, Fudan University, Shanghai, 200433, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Peng Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Jiajun Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China.
| | - Lingxiao Zhao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
| | - Bo Yin
- Department of Radiology, Huashan Hospital Affiliated to Fudan University, Shanghai, 200040, China.
| |
Collapse
|
7
|
Young LH, Kim J, Yakin M, Lin H, Dao DT, Kodati S, Sharma S, Lee AY, Lee CS, Sen HN. Automated Detection of Vascular Leakage in Fluorescein Angiography - A Proof of Concept. Transl Vis Sci Technol 2022; 11:19. [PMID: 35877095 PMCID: PMC9339697 DOI: 10.1167/tvst.11.7.19] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose The purpose of this paper was to develop a deep learning algorithm to detect retinal vascular leakage (leakage) in fluorescein angiography (FA) of patients with uveitis and use the trained algorithm to determine clinically notable leakage changes. Methods An algorithm was trained and tested to detect leakage on a set of 200 FA images (61 patients) and evaluated on a separate 50-image test set (21 patients). The ground truth was leakage segmentation by two clinicians. The Dice Similarity Coefficient (DSC) was used to measure concordance. Results During training, the algorithm achieved a best average DSC of 0.572 (95% confidence interval [CI] = 0.548–0.596). The trained algorithm achieved a DSC of 0.563 (95% CI = 0.543–0.582) when tested on an additional set of 50 images. The trained algorithm was then used to detect leakage on pairs of FA images from longitudinal patient visits. Longitudinal leakage follow-up showed a >2.21% change in the visible retina area covered by leakage (as detected by the algorithm) had a sensitivity and specificity of 90% (area under the curve [AUC] = 0.95) of detecting a clinically notable change compared to the gold standard, an expert clinician's assessment. Conclusions This deep learning algorithm showed modest concordance in identifying vascular leakage compared to ground truth but was able to aid in identifying vascular FA leakage changes over time. Translational Relevance This is a proof-of-concept study that vascular leakage can be detected in a more standardized way and that tools can be developed to help clinicians more objectively compare vascular leakage between FAs.
Collapse
Affiliation(s)
- LeAnne H Young
- National Eye Institute, Bethesda, MD, USA.,Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Jongwoo Kim
- National Library of Medicine, Bethesda, MD, USA
| | | | - Henry Lin
- National Eye Institute, Bethesda, MD, USA
| | | | | | - Sumit Sharma
- Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - H Nida Sen
- National Eye Institute, Bethesda, MD, USA
| |
Collapse
|
8
|
Ranjbar S, Singleton KW, Curtin L, Rickertsen CR, Paulson LE, Hu LS, Mitchell JR, Swanson KR. Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients. FRONTIERS IN NEUROIMAGING 2022; 1:832512. [PMID: 37555156 PMCID: PMC10406204 DOI: 10.3389/fnimg.2022.832512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/21/2022] [Indexed: 08/10/2023]
Abstract
Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.
Collapse
Affiliation(s)
- Sara Ranjbar
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Kyle W. Singleton
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Cassandra R. Rickertsen
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Lisa E. Paulson
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Leland S. Hu
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
- Department of Diagnostic Imaging and Interventional Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Joseph Ross Mitchell
- Department of Medicine, Faculty of Medicine & Dentistry and the Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
- Provincial Clinical Excellence Portfolio, Alberta Health Services, Edmonton, AB, Canada
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| |
Collapse
|
9
|
Fletcher E, DeCarli C, Fan AP, Knaack A. Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging. Front Neurosci 2021; 15:683426. [PMID: 34234642 PMCID: PMC8255694 DOI: 10.3389/fnins.2021.683426] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 05/27/2021] [Indexed: 01/18/2023] Open
Abstract
Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.
Collapse
Affiliation(s)
- Evan Fletcher
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Audrey P Fan
- Department of Neurology, University of California, Davis, Davis, CA, United States.,Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
| | - Alexander Knaack
- Department of Neurology, University of California, Davis, Davis, CA, United States
| |
Collapse
|
10
|
DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques. Neuroimage 2020; 227:117649. [PMID: 33338616 DOI: 10.1016/j.neuroimage.2020.117649] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/18/2023] Open
Abstract
As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.
Collapse
|
11
|
Debelee TG, Kebede SR, Schwenker F, Shewarega ZM. Deep Learning in Selected Cancers' Image Analysis-A Survey. J Imaging 2020; 6:121. [PMID: 34460565 PMCID: PMC8321208 DOI: 10.3390/jimaging6110121] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/19/2020] [Accepted: 10/26/2020] [Indexed: 02/08/2023] Open
Abstract
Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.
Collapse
Affiliation(s)
- Taye Girma Debelee
- Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia; (S.R.K.); (Z.M.S.)
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, 120611 Addis Ababa, Ethiopia
| | - Samuel Rahimeto Kebede
- Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia; (S.R.K.); (Z.M.S.)
- Department of Electrical and Computer Engineering, Debreberhan University, 445 Debre Berhan, Ethiopia
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, University of Ulm, 89081 Ulm, Germany;
| | | |
Collapse
|
12
|
White BR, Padawer-Curry JA, Cohen AS, Licht DJ, Yodh AG. Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice. BIOMEDICAL OPTICS EXPRESS 2019; 10:5952-5973. [PMID: 31799057 PMCID: PMC6865125 DOI: 10.1364/boe.10.005952] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/05/2019] [Accepted: 09/13/2019] [Indexed: 05/25/2023]
Abstract
Resting-state functional connectivity analysis using optical neuroimaging holds the potential to be a powerful bridge between mouse models of disease and clinical neurologic monitoring. However, analysis techniques specific to optical methods are rudimentary, and algorithms from magnetic resonance imaging are not always applicable to optics. We have developed visual processing tools to increase data quality, improve brain segmentation, and average across sessions with better field-of-view. We demonstrate improved performance using resting-state optical intrinsic signal from normal mice. The proposed methods increase the amount of usable data from neuroimaging studies, improve image fidelity, and should be translatable to human optical neuroimaging systems.
Collapse
Affiliation(s)
- Brian R. White
- Division of Pediatric Cardiology, Department of Pediatrics, The Children’s Hospital of Philadelphia. 3401 Civic Center Blvd., Pediatric Cardiology - 8NW, Philadelphia, PA 19104, USA
| | - Jonah A. Padawer-Curry
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of Philadelphia. 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Akiva S. Cohen
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia. 3615 Civic Center Blvd., Abramson Research Center, Room 816-H, Philadelphia, PA 19104, USA
| | - Daniel J. Licht
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of Philadelphia. 3501 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Arjun G. Yodh
- Department of Physics and Astronomy, University of Pennsylvania. 3231 Walnut St., Philadelphia, PA 19104, USA
| |
Collapse
|
13
|
Consistent validation of gray-level thresholding image segmentation algorithms based on machine learning classifiers. Stat Pap (Berl) 2019. [DOI: 10.1007/s00362-019-01138-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
14
|
Pishghadam M, Kazemi K, Nekooei S, Seilanian-Toosi F, Hoseini-Ghahfarokhi M, Zabizadeh M, Fatemi A. A new approach to automatic fetal brain extraction from MRI using a variational level set method. Med Phys 2019; 46:4983-4991. [PMID: 31419312 DOI: 10.1002/mp.13766] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 07/30/2019] [Accepted: 07/31/2019] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Appropriate images extracted from the MRI of mothers' wombs can be of great help in the medical diagnosis of fetal abnormalities. As maternal tissue may appear in such images, affecting visualization of myelination of the fetal brain, it is not possible to use methods routinely used for extraction of adult brains for fetal brains. The aim of the present study was to use a variational level set approach to extract fetal brain from T2-weighted MR images of the womb. METHODS Coronal T2-weighted images were acquired using fast MRI protocols (to avoid artifacts). The database includes 105 MR images from eight subjects. After correcting the inhomogeneity of the images, the fetal eyes were located, and from that information, the location of the fetus brain was automatically determined. Then, the variational level set was used for fetus brain extraction. The results were analyzed by a clinical specialist (radiologist) and the similarity (Dice and Jaccard coefficients), sensitivity and specificity were calculated. RESULTS AND CONCLUSIONS The means of the statistical analysis for the Dice and Jaccard coefficients, sensitivity and specificity, were 99.56%, 96.89%, 95.71%, and 97.96%, respectively. Thus, extraction of fetal brain from MR images was confirmed, both statistically and visually through cross-validation.
Collapse
Affiliation(s)
- Morteza Pishghadam
- Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Sirous Nekooei
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farrokh Seilanian-Toosi
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mojtaba Hoseini-Ghahfarokhi
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mansour Zabizadeh
- Department of Radiology and Nuclear Medicine, School of Para Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Fatemi
- Department of Radiation Oncology and Radiology, University of Mississippi Medical Center (UMMC), Jackson, MS, USA
| |
Collapse
|
15
|
Bahrani AA, Al-Janabi OM, Abner EL, Bardach SH, Kryscio RJ, Wilcock DM, Smith CD, Jicha GA. Post-acquisition processing confounds in brain volumetric quantification of white matter hyperintensities. J Neurosci Methods 2019; 327:108391. [PMID: 31408649 DOI: 10.1016/j.jneumeth.2019.108391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 08/03/2019] [Accepted: 08/03/2019] [Indexed: 01/18/2023]
Abstract
BACKGROUND Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored. NEW METHOD 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance. RESULTS A series of experiments using standard MRI acquisition sequences explored post-acquisition sources-of-variability in the quantification of WMH volumetric data. Sources-of-variability included: the choice of image center, software suite and version, thresholding selection, and manual editing procedures (when used). Controlling for the identified sources-of-variability led to a protocol with less than 0.5% variability between independent raters in post-acquisition WMH volumetric quantification. COMPARISON WITH EXISTING METHOD(S) Post-acquisition processing techniques can introduce an average variance approaching 15% in WMH volume quantification despite identical scan acquisitions. Understanding and controlling for such sources-of-variability can reduce post-acquisition quantitative image processing variance to less than 0.5%. DISCUSSION Considerations of potential sources-of-variability in MRI volume quantification techniques and reduction in such variability is imperative to allow for reliable cross-site and cross-study comparisons.
Collapse
Affiliation(s)
- Ahmed A Bahrani
- Department of Biomedical Engineering, College of Engineering, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
| | - Omar M Al-Janabi
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Erin L Abner
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Shoshana H Bardach
- Department of Gerontology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Richard J Kryscio
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Department of Statistics, College of Arts and Science, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Donna M Wilcock
- Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Charles D Smith
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Magnetic Resonance Imaging and Spectroscopy Center (MRISC), College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Gregory A Jicha
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
| |
Collapse
|
16
|
Huang Y, Datta A, Bikson M, Parra LC. Realistic volumetric-approach to simulate transcranial electric stimulation-ROAST-a fully automated open-source pipeline. J Neural Eng 2019; 16:056006. [PMID: 31071686 PMCID: PMC7328433 DOI: 10.1088/1741-2552/ab208d] [Citation(s) in RCA: 196] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement. APPROACH ROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline. MAIN RESULTS The electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS. SIGNIFICANCE We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.
Collapse
Affiliation(s)
- Yu Huang
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY 10031, United States of America. Research & Development, Soterix Medical Inc., New York, NY 10001, United States of America
| | | | | | | |
Collapse
|
17
|
Jain SK, Kumar D, Thakur M, Ray RK. Proximal Support Vector Machine-Based Hybrid Approach for Edge Detection in Noisy Images. JOURNAL OF INTELLIGENT SYSTEMS 2019. [DOI: 10.1515/jisys-2017-0566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
We propose a novel edge detector in the presence of Gaussian noise with the use of proximal support vector machine (PSVM). The edges of a noisy image are detected using a two-stage architecture: smoothing of image is first performed using regularized anisotropic diffusion, followed by the classification using PSVM, termed as regularized anisotropic diffusion-based PSVM (RAD-PSVM) method. In this process, a feature vector is formed for a pixel using the denoised coefficient’s class and the local orientations to detect edges in all possible directions in images. From the experiments, conducted on both synthetic and benchmark images, it is observed that our RAD-PSVM approach outperforms the other state-of-the-art edge detection approaches, both qualitatively and quantitatively.
Collapse
Affiliation(s)
- Subit K. Jain
- School of Basic Sciences, Indian Institute of Technology, Mandi, Himachal Pradesh, India
| | - Deepak Kumar
- School of Basic Sciences, Indian Institute of Technology, Mandi, Himachal Pradesh, India
| | - Manoj Thakur
- School of Basic Sciences, Indian Institute of Technology, Mandi, Himachal Pradesh, India
| | - Rajendra K. Ray
- School of Basic Sciences, Indian Institute of Technology, Mandi, Himachal Pradesh, India, Phone: +91-1905-267041
| |
Collapse
|
18
|
Boucher MA, Lippé S, Dupont C, Knoth IS, Lopez G, Shams R, El-Jalbout R, Damphousse A, Kadoury S. Computer-aided lateral ventricular and brain volume measurements in 3D ultrasound for assessing growth trajectories in newborns and neonates. ACTA ACUST UNITED AC 2018; 63:225012. [DOI: 10.1088/1361-6560/aaea85] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
19
|
Ou Y, Zöllei L, Da X, Retzepi K, Murphy SN, Gerstner ER, Rosen BR, Grant PE, Kalpathy-Cramer J, Gollub RL. Field of View Normalization in Multi-Site Brain MRI. Neuroinformatics 2018; 16:431-444. [PMID: 29353341 PMCID: PMC7334884 DOI: 10.1007/s12021-018-9359-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .
Collapse
Affiliation(s)
- Yangming Ou
- Department of Pediatrics and Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lilla Zöllei
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Xiao Da
- Functional Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kallirroi Retzepi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shawn N Murphy
- Research Computing, Partners Healthcare, Boston, MA, USA
| | - Elizabeth R Gerstner
- Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce R Rosen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Department of Pediatrics and Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Randy L Gollub
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
20
|
Zhao G, Liu F, Oler JA, Meyerand ME, Kalin NH, Birn RM. Bayesian convolutional neural network based MRI brain extraction on nonhuman primates. Neuroimage 2018; 175:32-44. [PMID: 29604454 PMCID: PMC6095475 DOI: 10.1016/j.neuroimage.2018.03.065] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 03/26/2018] [Accepted: 03/27/2018] [Indexed: 11/17/2022] Open
Abstract
Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10-4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases.
Collapse
Affiliation(s)
- Gengyan Zhao
- Department of Medical Physics, University of Wisconsin - Madison, USA.
| | - Fang Liu
- Department of Radiology, University of Wisconsin - Madison, USA
| | - Jonathan A Oler
- Department of Psychiatry, University of Wisconsin - Madison, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin - Madison, USA; Department of Biomedical Engineering, University of Wisconsin - Madison, USA
| | - Ned H Kalin
- Department of Psychiatry, University of Wisconsin - Madison, USA
| | - Rasmus M Birn
- Department of Medical Physics, University of Wisconsin - Madison, USA; Department of Psychiatry, University of Wisconsin - Madison, USA
| |
Collapse
|
21
|
Novosad P, Collins DL. An efficient and accurate method for robust inter-dataset brain extraction and comparisons with 9 other methods. Hum Brain Mapp 2018; 39:4241-4257. [PMID: 29972616 DOI: 10.1002/hbm.24243] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 05/17/2018] [Accepted: 05/27/2018] [Indexed: 01/18/2023] Open
Abstract
Brain extraction is an important first step in many magnetic resonance neuroimaging studies. Due to variability in brain morphology and in the appearance of the brain due to differences in scanner acquisition parameters, the development of a generally applicable brain extraction algorithm has proven challenging. Learning-based brain extraction algorithms in particular perform well when the target and training images are sufficiently similar, but often perform worse when this condition is not met. In this study, we propose a new patch-based multi-atlas segmentation method for brain extraction which is specifically developed for accurate and robust processing across datasets. Using a diverse collection of labeled images from 5 different datasets, extensive comparisons were made with 9 other commonly used brain extraction methods, both before and after applying error correction (a machine learning method for automatically correcting segmentation errors) to each method. The proposed method performed equal to or better than the other methods in each of two segmentation scenarios: a challenging inter-dataset segmentation scenario in which no dataset-specific atlases were used (mean Dice coefficient 98.57%, volumetric correlation 0.994 across datasets following error correction), and an intra-dataset segmentation scenario in which only dataset-specific atlases were used (mean Dice coefficient 99.02%, volumetric correlation 0.998 across datasets following error correction). Furthermore, combined with error correction, the proposed method runs in less than one-tenth of the time required by the other top-performing methods in the challenging inter-dataset comparisons. Validation on an independent multi-centre dataset also confirmed the excellent performance of the proposed method.
Collapse
Affiliation(s)
- Philip Novosad
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | | |
Collapse
|
22
|
Mohseni Salehi SS, Erdogmus D, Gholipour A. Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2319-2330. [PMID: 28678704 PMCID: PMC5715475 DOI: 10.1109/tmi.2017.2721362] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.
Collapse
|
23
|
Maier O, Menze BH, von der Gablentz J, Ḧani L, Heinrich MP, Liebrand M, Winzeck S, Basit A, Bentley P, Chen L, Christiaens D, Dutil F, Egger K, Feng C, Glocker B, Götz M, Haeck T, Halme HL, Havaei M, Iftekharuddin KM, Jodoin PM, Kamnitsas K, Kellner E, Korvenoja A, Larochelle H, Ledig C, Lee JH, Maes F, Mahmood Q, Maier-Hein KH, McKinley R, Muschelli J, Pal C, Pei L, Rangarajan JR, Reza SMS, Robben D, Rueckert D, Salli E, Suetens P, Wang CW, Wilms M, Kirschke JS, Kr̈amer UM, Münte TF, Schramm P, Wiest R, Handels H, Reyes M. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 2017; 35:250-269. [PMID: 27475911 PMCID: PMC5099118 DOI: 10.1016/j.media.2016.07.009] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 05/30/2016] [Accepted: 07/20/2016] [Indexed: 01/14/2023]
Abstract
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
Collapse
Affiliation(s)
- Oskar Maier
- Institut for Medical Informatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Live Science, University of Lübeck, Germany
| | - Bjoern H Menze
- Institute for Advanced Study and Department of Computer Science, Technische Universität München, Munich, Germany
| | | | - Levin Ḧani
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | | | | | - Stefan Winzeck
- Institute for Advanced Study and Department of Computer Science, Technische Universität München, Munich, Germany
| | - Abdul Basit
- Pakistan Institute of Nuclear Science and Technology, Islamabad, Pakistan
| | - Paul Bentley
- Division of Brain Sciences, Department of Medicine, Imperial College London, UK
| | - Liang Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
- Division of Brain Sciences, Department of Medicine, Imperial College London, UK
| | - Daan Christiaens
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | | | - Karl Egger
- Department of Neuroradiology, University Medical Center Freiburg, Germany
| | - Chaolu Feng
- College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Michael Götz
- Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Tom Haeck
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Hanna-Leena Halme
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering NBE, Aalto University School of Science, Aalto, Finland
| | | | - Khan M Iftekharuddin
- Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | | | | | - Elias Kellner
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany
| | - Antti Korvenoja
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | - Christian Ledig
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Jia-Hong Lee
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - Frederik Maes
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Qaiser Mahmood
- Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden
- Pakistan Institute of Nuclear Science and Technology, Islamabad, Pakistan
| | - Klaus H Maier-Hein
- Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Richard McKinley
- Department of Diagnostic and Interventional Neuroradiology, Inselspital Bern, Switzerland
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chris Pal
- Ecole Polytechnique de Montréal, Canada
| | - Linmin Pei
- Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - Janaki Raman Rangarajan
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Syed M S Reza
- Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - David Robben
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Eero Salli
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Paul Suetens
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Belgium
| | - Ching-Wei Wang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan
| | - Matthias Wilms
- Institut for Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Ulrike M Kr̈amer
- Department of Neurology, University of Lübeck, Germany
- Institute of Psychology II, University of Lübeck, Germany
| | | | - Peter Schramm
- Institute of Neuroradiology, University Medical Center Lübeck
| | - Roland Wiest
- Department of Diagnostic and Interventional Neuroradiology, Inselspital Bern, Switzerland
| | - Heinz Handels
- Institut for Medical Informatics, University of Lübeck, Lübeck, Germany
| | - Mauricio Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| |
Collapse
|
24
|
Jimenez-Del-Toro O, Muller H, Krenn M, Gruenberg K, Taha AA, Winterstein M, Eggel I, Foncubierta-Rodriguez A, Goksel O, Jakab A, Kontokotsios G, Langs G, Menze BH, Salas Fernandez T, Schaer R, Walleyo A, Weber MA, Dicente Cid Y, Gass T, Heinrich M, Jia F, Kahl F, Kechichian R, Mai D, Spanier AB, Vincent G, Wang C, Wyeth D, Hanbury A. Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2459-2475. [PMID: 27305669 DOI: 10.1109/tmi.2016.2578680] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.
Collapse
|
25
|
Paholpak P, Carr AR, Barsuglia JP, Barrows RJ, Jimenez E, Lee GJ, Mendez MF. Person-Based Versus Generalized Impulsivity Disinhibition in Frontotemporal Dementia and Alzheimer Disease. J Geriatr Psychiatry Neurol 2016; 29:344-351. [PMID: 27647788 DOI: 10.1177/0891988716666377] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND While much disinhibition in dementia results from generalized impulsivity, in behavioral variant frontotemporal dementia (bvFTD) disinhibition may also result from impaired social cognition. OBJECTIVE To deconstruct disinhibition and its neural correlates in bvFTD vs. early-onset Alzheimer's disease (eAD). METHODS Caregivers of 16 bvFTD and 21 matched-eAD patients completed the Frontal Systems Behavior Scale disinhibition items. The disinhibition items were further categorized into (1) "person-based" subscale which predominantly associated with violating social propriety and personal boundary and (2) "generalized-impulsivity" subscale which included nonspecific impulsive acts. Subscale scores were correlated with grey matter volumes from tensor-based morphometry on magnetic resonance images. RESULTS In comparison to the eAD patients, the bvFTD patients developed greater person-based disinhibition ( P < 0.001) but comparable generalized impulsivity. Severity of person-based disinhibition significantly correlated with the left anterior superior temporal sulcus (STS), and generalized-impulsivity correlated with the right orbitofrontal cortex (OFC) and the left anterior temporal lobe (aTL). CONCLUSIONS Person-based disinhibition was predominant in bvFTD and correlated with the left STS. In both dementia, violations of social propriety and personal boundaries involved fronto-parieto-temporal network of Theory of Mind, whereas nonspecific disinhibition involved the OFC and aTL.
Collapse
Affiliation(s)
- Pongsatorn Paholpak
- 1 Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, CA, USA.,2 Department of Psychiatry, Khon Kaen University, Khon Kaen, Thailand
| | - Andrew R Carr
- 1 Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, CA, USA.,3 Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA
| | | | - Robin J Barrows
- 1 Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, CA, USA.,3 Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA
| | - Elvira Jimenez
- 1 Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, CA, USA.,3 Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA.,4 Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California at Los Angeles, CA, USA
| | - Grace J Lee
- 5 Department of Psychology, School of Behavioral Health, Loma Linda University, Loma Linda, CA, USA
| | - Mario F Mendez
- 1 Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, CA, USA.,3 Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA.,4 Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California at Los Angeles, CA, USA
| |
Collapse
|
26
|
Kim J, Valdés Hernández MDC, Royle NA, Maniega SM, Aribisala BS, Gow AJ, Bastin ME, Deary IJ, Wardlaw JM, Park J. 3D shape analysis of the brain's third ventricle using a midplane encoded symmetric template model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 129:51-62. [PMID: 27084320 PMCID: PMC4841787 DOI: 10.1016/j.cmpb.2016.02.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/12/2016] [Accepted: 02/22/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases. To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures, shape analysis is a promising approach. However, there are hurdles in modeling the third ventricle shape. First, it has topological variations across individuals due to the inter-thalamic adhesion. In addition, as an interhemispheric structure, it needs to be aligned to the midsagittal plane to assess its asymmetric and regional deformation. METHOD To address these issues, we propose a model-based shape assessment. Our template model of the third ventricle consists of a midplane and a symmetric mesh of generic shape. By mapping the template's midplane to the individuals' brain midsagittal plane, we align the symmetric mesh on the midline of the brain before quantifying the third ventricle shape. To build the vertex-wise correspondence between the individual third ventricle and the template mesh, we employ a minimal-distortion surface deformation framework. In addition, to account for topological variations, we implement geometric constraints guiding the template mesh to have zero width where the inter-thalamic adhesion passes through, preventing vertices crossing between left and right walls of the third ventricle. The individual shapes are compared using a vertex-wise deformity from the symmetric template. RESULTS Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume (i.e. proxy for general brain atrophy), gender and the fluid intelligence at age 72. It also revealed that the proposed method can detect the regional and asymmetrical deformation unlike the conventional measures: volume (median 1.95ml, IQR 0.96ml) and width of the third ventricle. Similarity measures between binary masks and the shape model showed that the latter reconstructed shape details with high accuracy (Dice coefficient ≥0.9, mean distance 0.5mm and Hausdorff distance 2.7mm). CONCLUSIONS We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle, providing high accuracy and inter-subject consistency in the shape quantification. This shape modeling method with geometric constraints based on anatomical landmarks could be extended to other brain structures which require a consistent measurement basis in the morphometry.
Collapse
Affiliation(s)
- Jaeil Kim
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Maria del C Valdés Hernández
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Natalie A Royle
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Susana Muñoz Maniega
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Benjamin S Aribisala
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK; Computer Science Department, Lagos State University, Nigeria
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK
| | - Mark E Bastin
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Jinah Park
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
| |
Collapse
|
27
|
Panchal H, Paholpak P, Lee G, Carr A, Barsuglia JP, Mather M, Jimenez E, Mendez MF. Neuropsychological and Neuroanatomical Correlates of the Social Norms Questionnaire in Frontotemporal Dementia Versus Alzheimer's Disease. Am J Alzheimers Dis Other Demen 2016; 31:326-32. [PMID: 26646114 PMCID: PMC10852706 DOI: 10.1177/1533317515617722] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Traditional neuropsychological batteries may not distinguish early behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) without the inclusion of a social behavioral measure. We compared 33 participants, 15 bvFTD, and 18 matched patients with early-onset AD (eAD), on the Social Norms Questionnaire (SNQ), neuropsychological tests and 3-dimensional T1-weighted magnetic resonance imaging (MRI). The analyses included correlations of SNQ results (total score, overendorsement or "overadhere" errors, and violations or "break" errors) with neuropsychological results and tensor-based morphometry regions of interest. Patients with BvFTD had significantly lower SNQ total scores and higher overadhere errors than patients with eAD. On neuropsychological measures, the SNQ total scores correlated significantly with semantic knowledge and the overadhere subscores with executive dysfunction. On MRI analysis, the break subscores significantly correlated with lower volume of lateral anterior temporal lobes (aTL). The results also suggest that endorsement of social norm violations corresponds to the role of the right aTL in social semantic knowledge.
Collapse
Affiliation(s)
- Hemali Panchal
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Department of Neurology, Los Angeles, CA, USA
| | - Pongsatorn Paholpak
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Department of Neurology, Los Angeles, CA, USA Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, CA, USA Department of Psychiatry, Khon Kaen University, Khon Khaen, Thailand
| | - Grace Lee
- Department of Psychology, School of Behavioral Health, Loma Linda, CA, USA
| | - Andrew Carr
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | | | - Michelle Mather
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Department of Neurology, Los Angeles, CA, USA
| | - Elvira Jimenez
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Department of Neurology, Los Angeles, CA, USA Department of Psychiatry & Biobehavioral Sciences, Los Angeles, CA, USA
| | - Mario F Mendez
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Department of Neurology, Los Angeles, CA, USA Department of Psychiatry & Biobehavioral Sciences, Los Angeles, CA, USA
| |
Collapse
|
28
|
Abstract
The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.
Collapse
Affiliation(s)
- P. Kalavathi
- />Department of Computer Science and Applications, Gandhigram Rural Institute - Deemed University, Gandhigram, Tamil Nadu 624302 India
| | - V. B. Surya Prasath
- />Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211 USA
| |
Collapse
|
29
|
Alansary A, Ismail M, Soliman A, Khalifa F, Nitzken M, Elnakib A, Mostapha M, Black A, Stinebruner K, Casanova MF, Zurada JM, El-Baz A. Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models. IEEE J Biomed Health Inform 2016; 20:925-935. [DOI: 10.1109/jbhi.2015.2415477] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
30
|
Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods. Sci Rep 2016; 6:23470. [PMID: 27010238 PMCID: PMC4806304 DOI: 10.1038/srep23470] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 03/08/2016] [Indexed: 02/04/2023] Open
Abstract
Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.
Collapse
|
31
|
Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, Biller A. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. Neuroimage 2016; 129:460-469. [PMID: 26808333 DOI: 10.1016/j.neuroimage.2016.01.024] [Citation(s) in RCA: 283] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 01/10/2016] [Accepted: 01/11/2016] [Indexed: 01/18/2023] Open
Abstract
Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N=53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N=135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials.
Collapse
Affiliation(s)
- Jens Kleesiek
- MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Heidelberg University HCI/IWR, Heidelberg, Germany; Division of Radiology, German Cancer Research Center, Heidelberg, Germany.
| | - Gregor Urban
- MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Hubert
- MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Daniel Schwarz
- MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Junior Group Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Armin Biller
- MDMI Lab, Division of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| |
Collapse
|
32
|
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:813696. [PMID: 26759553 PMCID: PMC4680055 DOI: 10.1155/2015/813696] [Citation(s) in RCA: 110] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 08/19/2015] [Indexed: 12/03/2022]
Abstract
Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
Collapse
|
33
|
McCarthy CS, Ramprashad A, Thompson C, Botti JA, Coman IL, Kates WR. A comparison of FreeSurfer-generated data with and without manual intervention. Front Neurosci 2015; 9:379. [PMID: 26539075 PMCID: PMC4612506 DOI: 10.3389/fnins.2015.00379] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 09/29/2015] [Indexed: 01/18/2023] Open
Abstract
This paper examined whether FreeSurfer-generated data differed between a fully-automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation. In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. Potential exceptions to and limitations of these conclusions are discussed.
Collapse
Affiliation(s)
- Christopher S McCarthy
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Avinash Ramprashad
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Carlie Thompson
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Jo-Anna Botti
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Ioana L Coman
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Neuroimaging, State University of New York at Upstate Medical University Syracuse, NY, USA
| |
Collapse
|
34
|
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Ç, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1993-2024. [PMID: 25494501 PMCID: PMC4833122 DOI: 10.1109/tmi.2014.2377694] [Citation(s) in RCA: 1699] [Impact Index Per Article: 188.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
Collapse
|
35
|
Yadollahi M, Procházka A, Kašparová M, Vyšata O, Mařík V. Separation of overlapping dental arch objects using digital records of illuminated plaster casts. Biomed Eng Online 2015; 14:67. [PMID: 26162755 PMCID: PMC4499221 DOI: 10.1186/s12938-015-0066-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2015] [Accepted: 06/29/2015] [Indexed: 11/18/2022] Open
Abstract
Background Plaster casts of individual patients are important for orthodontic specialists during the treatment process and their analysis is still a standard diagnostical tool. But the growing capabilities of information technology enable their replacement by digital models obtained by complex scanning systems. Method This paper presents the possibility of using a digital camera as a simple instrument to obtain the set of digital images for analysis and evaluation of the treatment using appropriate mathematical tools of image processing. The methods studied in this paper include the segmentation of overlapping dental bodies and the use of different illumination sources to increase the reliability of the separation process. The circular Hough transform, region growing with multiple seed points, and the convex hull detection method are applied to the segmentation of orthodontic plaster cast images to identify dental arch objects and their sizes. Results The proposed algorithm presents the methodology of improving the accuracy of segmentation of dental arch components using combined illumination sources. Dental arch parameters and distances between the canines and premolars for different segmentation methods were used as a measure to compare the results obtained. Conclusion A new method of segmentation of overlapping dental arch components using digital records of illuminated plaster casts provides information with the precision required for orthodontic treatment. The distance between corresponding teeth was evaluated with a mean error of 1.38% and the Dice similarity coefficient of the evaluated dental bodies boundaries reached 0.9436 with a false positive rate \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$FPR=0.0381$$\end{document}FPR=0.0381 and false negative rate \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$FNR=0.0728$$\end{document}FNR=0.0728.
Collapse
Affiliation(s)
- Mohammadreza Yadollahi
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic.
| | - Aleš Procházka
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. .,Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Zikova 1903/4, 166 36, Prague 6, Czech Republic.
| | - Magdaléna Kašparová
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. .,Department of Paediatric Stomatology, The Second Medical Faculty, Charles University, V Úvalu 84, 150 06, Prague 5, Czech Republic.
| | - Oldřich Vyšata
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. .,Department of Neurology, Charles University, Sokolská 581, 500 05, Hradec Králové, Czech Republic.
| | - Vladimír Mařík
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Zikova 1903/4, 166 36, Prague 6, Czech Republic.
| |
Collapse
|
36
|
Heckemann RA, Ledig C, Gray KR, Aljabar P, Rueckert D, Hajnal JV, Hammers A. Brain Extraction Using Label Propagation and Group Agreement: Pincram. PLoS One 2015; 10:e0129211. [PMID: 26161961 PMCID: PMC4498771 DOI: 10.1371/journal.pone.0129211] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2014] [Accepted: 05/06/2015] [Indexed: 01/18/2023] Open
Abstract
Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the "success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.
Collapse
Affiliation(s)
- Rolf A. Heckemann
- MedTech West at Sahlgrenska University Hospital, Gothenburg, Sweden
- Institute of Neuroscience and Physiology, Gothenburg University, Gothenburg, Sweden
- Centre for Brain Sciences, Imperial College, London, United Kingdom
- The Neurodis Foundation, Lyon, France
- * E-mail:
| | - Christian Ledig
- Department of Computing, Imperial College, London, United Kingdom
| | | | - Paul Aljabar
- Department of Computing, Imperial College, London, United Kingdom
- Imaging Sciences and Biomedical Engineering, King’s College, London, United Kingdom
| | - Daniel Rueckert
- Department of Computing, Imperial College, London, United Kingdom
| | - Joseph V. Hajnal
- Imaging Sciences and Biomedical Engineering, King’s College, London, United Kingdom
| | - Alexander Hammers
- The Neurodis Foundation, Lyon, France
- Imaging Sciences and Biomedical Engineering, King’s College, London, United Kingdom
| |
Collapse
|
37
|
Fuzzy Index to Evaluate Edge Detection in Digital Images. PLoS One 2015; 10:e0131161. [PMID: 26115362 PMCID: PMC4483257 DOI: 10.1371/journal.pone.0131161] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 05/29/2015] [Indexed: 11/29/2022] Open
Abstract
In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard’s index (JI) and Dice’s coefficient (DC). These metrics compare two images, the first one is the reference edges image, and the second one is the detected edges image. It is important to mention that all existing metrics must binarize images before their evaluation. Binarization step causes information to be lost because an incomplete image is being evaluated. In this paper, we propose a fuzzy index (FI) for edge evaluation that does not use a binarization step. In order to process all detected edges, images are represented in their fuzzy form and all calculations are made with fuzzy sets operators and fuzzy Euclidean distance between both images. Our proposed index is compared to the most used metrics using synthetic images, with good results.
Collapse
|
38
|
Kim J, Valdes-Hernandez MDC, Royle NA, Park J. Hippocampal Shape Modeling Based on a Progressive Template Surface Deformation and its Verification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1242-1261. [PMID: 25532173 DOI: 10.1109/tmi.2014.2382581] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Accurately recovering the hippocampal shapes against rough and noisy segmentations is as challenging as achieving good anatomical correspondence between the individual shapes. To address these issues, we propose a mesh-to-volume registration approach, characterized by a progressive model deformation. Our model implements flexible weighting scheme for model rigidity under a multi-level neighborhood for vertex connectivity. This method induces a large-to-small scale deformation of a template surface to build the pairwise correspondence by minimizing geometric distortion while robustly restoring the individuals' shape characteristics. We evaluated the proposed method's (1) accuracy and robustness in smooth surface reconstruction, (2) sensitivity in detecting significant shape differences between healthy control and disease groups (mild cognitive impairment and Alzheimer's disease), (3) robustness in constructing the anatomical correspondence between individual shape models, and (4) applicability in identifying subtle shape changes in relation to cognitive abilities in a healthy population. We compared the performance of the proposed method with other well-known methods--SPHARM-PDM, ShapeWorks and LDDMM volume registration with template injection--using various metrics of shape similarity, surface roughness, volume, and shape deformity. The experimental results showed that the proposed method generated smooth surfaces with less volume differences and better shape similarity to input volumes than others. The statistical analyses with clinical variables also showed that it was sensitive in detecting subtle shape changes of hippocampus.
Collapse
|
39
|
Huang Y, Parra LC. Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed. PLoS One 2015; 10:e0125477. [PMID: 25992793 PMCID: PMC4436344 DOI: 10.1371/journal.pone.0125477] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 03/24/2015] [Indexed: 11/25/2022] Open
Abstract
Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS). The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). The first step in generating such models is to obtain an accurate segmentation of individual head anatomy, including not only brain but also cerebrospinal fluid (CSF), skull and soft tissues, with a field of view (FOV) that covers the whole head. Currently available automated segmentation tools only provide results for brain tissues, have a limited FOV, and do not guarantee continuity and smoothness of tissues, which is crucially important for accurate current-flow estimates. Here we present a tool that addresses these needs. It is based on a rigorous Bayesian inference framework that combines image intensity model, anatomical prior (atlas) and morphological constraints using Markov random fields (MRF). The method is evaluated on 20 simulated and 8 real head volumes acquired with magnetic resonance imaging (MRI) at 1 mm3 resolution. We find improved surface smoothness and continuity as compared to the segmentation algorithms currently implemented in Statistical Parametric Mapping (SPM). With this tool, accurate and morphologically correct modeling of the whole-head anatomy for individual subjects may now be feasible on a routine basis. Code and data are fully integrated into SPM software tool and are made publicly available. In addition, a review on the MRI segmentation using atlas and the MRF over the last 20 years is also provided, with the general mathematical framework clearly derived.
Collapse
Affiliation(s)
- Yu Huang
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY, USA
| | - Lucas C. Parra
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY, USA
| |
Collapse
|
40
|
Punga MV, Gaurav R, Moraru L. Level set method coupled with Energy Image features for brain MR image segmentation. ACTA ACUST UNITED AC 2015; 59:219-29. [PMID: 24598830 DOI: 10.1515/bmt-2013-0111] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Accepted: 02/10/2014] [Indexed: 11/15/2022]
Abstract
Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.
Collapse
|
41
|
Tohka J. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review. World J Radiol 2014; 6:855-864. [PMID: 25431640 PMCID: PMC4241492 DOI: 10.4329/wjr.v6.i11.855] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 09/03/2014] [Accepted: 09/24/2014] [Indexed: 02/06/2023] Open
Abstract
Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.
Collapse
|
42
|
Patterson DK, Van Petten C, Beeson PM, Rapcsak SZ, Plante E. Bidirectional iterative parcellation of diffusion weighted imaging data: separating cortical regions connected by the arcuate fasciculus and extreme capsule. Neuroimage 2014; 102 Pt 2:704-16. [PMID: 25173414 PMCID: PMC4253691 DOI: 10.1016/j.neuroimage.2014.08.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Revised: 07/01/2014] [Accepted: 08/20/2014] [Indexed: 10/24/2022] Open
Abstract
This paper introduces a Bidirectional Iterative Parcellation (BIP) procedure designed to identify the location and size of connected cortical regions (parcellations) at both ends of a white matter tract in diffusion weighted images. The procedure applies the FSL option "probabilistic tracking with classification targets" in a bidirectional and iterative manner. To assess the utility of BIP, we applied the procedure to the problem of parcellating a limited set of well-established gray matter seed regions associated with the dorsal (arcuate fasciculus/superior longitudinal fasciculus) and ventral (extreme capsule fiber system) white matter tracts in the language networks of 97 participants. These left hemisphere seed regions and the two white matter tracts, along with their right hemisphere homologues, provided an excellent test case for BIP because the resulting parcellations overlap and their connectivity via the arcuate fasciculi and extreme capsule fiber systems are well studied. The procedure yielded both confirmatory and novel findings. Specifically, BIP confirmed that each tract connects within the seed regions in unique, but expected ways. Novel findings included increasingly left-lateralized parcellations associated with the arcuate fasciculus/superior longitudinal fasciculus as a function of age and education. These results demonstrate that BIP is an easily implemented technique that successfully confirmed cortical connectivity patterns predicted in the literature, and has the potential to provide new insights regarding the architecture of the brain.
Collapse
Affiliation(s)
- Dianne K Patterson
- The University of Arizona, Department of Speech, Language, and Hearing Sciences, Tucson, AZ, USA.
| | - Cyma Van Petten
- Binghamton University, Department of Psychology, Binghamton, NY, USA
| | - Pélagie M Beeson
- The University of Arizona, Department of Speech, Language, and Hearing Sciences, Tucson, AZ, USA; The University of Arizona, Department of Neurology, Tucson, AZ, USA
| | - Steven Z Rapcsak
- The University of Arizona, Department of Neurology, Tucson, AZ, USA
| | - Elena Plante
- The University of Arizona, Department of Speech, Language, and Hearing Sciences, Tucson, AZ, USA
| |
Collapse
|
43
|
Automated segmentation of multifocal basal ganglia T2*-weighted MRI hypointensities. Neuroimage 2014; 105:332-46. [PMID: 25451469 PMCID: PMC4275576 DOI: 10.1016/j.neuroimage.2014.10.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2014] [Revised: 09/08/2014] [Accepted: 10/03/2014] [Indexed: 12/17/2022] Open
Abstract
Multifocal basal ganglia T2*-weighted (T2*w) hypointensities, which are believed to arise mainly from vascular mineralization, were recently proposed as a novel MRI biomarker for small vessel disease and ageing. These T2*w hypointensities are typically segmented semi-automatically, which is time consuming, associated with a high intra-rater variability and low inter-rater agreement. To address these limitations, we developed a fully automated, unsupervised segmentation method for basal ganglia T2*w hypointensities. This method requires conventional, co-registered T2*w and T1-weighted (T1w) volumes, as well as region-of-interest (ROI) masks for the basal ganglia and adjacent internal capsule generated automatically from T1w MRI. The basal ganglia T2*w hypointensities were then segmented with thresholds derived with an adaptive outlier detection method from respective bivariate T2*w/T1w intensity distributions in each ROI. Artefacts were reduced by filtering connected components in the initial masks based on their standardised T2*w intensity variance. The segmentation method was validated using a custom-built phantom containing mineral deposit models, i.e. gel beads doped with 3 different contrast agents in 7 different concentrations, as well as with MRI data from 98 community-dwelling older subjects in their seventies with a wide range of basal ganglia T2*w hypointensities. The method produced basal ganglia T2*w hypointensity masks that were in substantial volumetric and spatial agreement with those generated by an experienced rater (Jaccard index = 0.62 ± 0.40). These promising results suggest that this method may have use in automatic segmentation of basal ganglia T2*w hypointensities in studies of small vessel disease and ageing. A novel method segmented focal T2*-weighted MRI hypointensities automatically. The method was validated with MRI of a novel phantom and 98 elderly subjects. The subject masks from the method and an experienced rater overlapped substantially. The method is potentially useful for research into small vessel disease and ageing.
Collapse
|
44
|
Nonlocal intracranial cavity extraction. Int J Biomed Imaging 2014; 2014:820205. [PMID: 25328511 PMCID: PMC4195262 DOI: 10.1155/2014/820205] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Accepted: 08/28/2014] [Indexed: 01/18/2023] Open
Abstract
Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.
Collapse
|
45
|
Prasad G, Joshi AA, Feng A, Toga AW, Thompson PM, Terzopoulos D. Skull-stripping with machine learning deformable organisms. J Neurosci Methods 2014; 236:114-24. [PMID: 25124851 DOI: 10.1016/j.jneumeth.2014.07.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Revised: 07/07/2014] [Accepted: 07/30/2014] [Indexed: 11/17/2022]
Abstract
BACKGROUND Segmentation methods for medical images may not generalize well to new data sets or new tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. We validate our framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping). NEW METHOD Our method borrows ideas from artificial life to govern a set of deformable models. We use control processes such as sensing, proactive planning, reactive behavior, and knowledge representation to segment an image. The image may have landmarks and features specific to that dataset; these may be easily incorporated into the plan. In addition, we use a machine learning method to make our segmentation more accurate. RESULTS Our method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error and performed on par to skull-stripping specific method on other metrics. COMPARISON WITH EXISTING METHOD(S) We tested our method on 838 T1-weighted images, evaluating results using distance and overlap error metrics based on expert gold standard segmentations. We evaluated the results before and after the learning step to quantify its benefit; we also compare our results to three other widely used methods: BSE, BET, and the Hybrid Watershed algorithm. CONCLUSIONS Our framework captures diverse categories of information needed for brain segmentation and will provide a foundation for tackling a wealth of segmentation problems.
Collapse
Affiliation(s)
- Gautam Prasad
- Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA.
| | - Anand A Joshi
- Signal and Image Processing Institute, USC, Los Angeles, CA, USA
| | - Albert Feng
- Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Arthur W Toga
- Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Ophthalmology, Neurology, Psychiatry & Behavioral Sciences, Radiology, and Biomedical Engineering, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Ophthalmology, Neurology, Psychiatry & Behavioral Sciences, Radiology, and Biomedical Engineering, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Pediatrics, Keck School of Medicine of USC, Los Angeles, CA, USA
| | | |
Collapse
|
46
|
Esteban O, Wollny G, Gorthi S, Ledesma-Carbayo MJ, Thiran JP, Santos A, Bach-Cuadra M. MBIS: multivariate Bayesian image segmentation tool. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 115:76-94. [PMID: 24768617 DOI: 10.1016/j.cmpb.2014.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Revised: 01/29/2014] [Accepted: 03/17/2014] [Indexed: 06/03/2023]
Abstract
We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.
Collapse
Affiliation(s)
- Oscar Esteban
- Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain.
| | - Gert Wollny
- Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Subrahmanyam Gorthi
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - María-J Ledesma-Carbayo
- Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Department of Radiology, Centre d'Imaginerie Biomédicale, University Hospital Center and University of Lausanne, Switzerland
| | - Andrés Santos
- Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Meritxell Bach-Cuadra
- Department of Radiology, Centre d'Imaginerie Biomédicale, University Hospital Center and University of Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| |
Collapse
|
47
|
Price CC, Tanner JJ, Schmalfuss I, Garvan CW, Gearen P, Dickey D, Heilman K, McDonagh DL, Libon DJ, Leonard C, Bowers D, Monk TG. A pilot study evaluating presurgery neuroanatomical biomarkers for postoperative cognitive decline after total knee arthroplasty in older adults. Anesthesiology 2014; 120:601-13. [PMID: 24534857 DOI: 10.1097/aln.0000000000000080] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Total knee arthroplasty improves quality of life but is associated with postoperative cognitive dysfunction in older adults. This prospective longitudinal pilot study with a parallel control group tested the hypotheses that (1) nondemented adults would exhibit primary memory and executive difficulties after total knee arthroplasty, and (2) reduced preoperative hippocampus/entorhinal volume would predict postoperative memory change, whereas preoperative leukoaraiosis and lacunae volumes would predict postoperative executive dysfunction. METHODS Surgery (n = 40) and age-education-matched controls with osteoarthritis (n = 15) completed pre- and postoperative (3 weeks, 3 months, and 1 yr) memory and cognitive testing. Hypothesized brain regions of interest were measured in patients completing preoperative magnetic resonance scans (surgery, n = 31; control, n = 12). Analyses used reliable change methods to identify the frequency of cognitive change at each time point. RESULTS The incidence of postoperative memory difficulties was shown with delay test indices (i.e., story memory test: 3 weeks = 17%, 3 months = 25%, 1 yr = 9%). Postoperative executive difficulty with measures of inhibitory function (i.e., Stroop Color Word: 3 weeks = 21%, 3 months = 22%, 1 yr = 9%). Hierarchical regression analysis assessing the predictive interaction of group (surgery, control) and preoperative neuroanatomical structures on decline showed that greater preoperative volumes of leukoaraiosis/lacunae were significantly contributed to postoperative executive (inhibitory) declines. CONCLUSIONS This pilot study suggests that executive and memory declines occur in nondemented adults undergoing orthopedic surgery. Severity of preoperative cerebrovascular disease may be relevant for understanding executive decline, in particular.
Collapse
Affiliation(s)
- Catherine C Price
- From the Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida (C.C.P., J.J.T., D.D., and D.B.); Joint Appointment, Department of Anesthesiology, University of Florida, Gainesville, Florida (C.C.P.); Department of Radiology, University of Florida, Gainesville, Florida (I.S.); Department of Radiology, North Florida South Georgia Veteran Association, Gainesville, Florida (I.S.); Health Science Center, University of Florida, Gainesville, Florida (C.W.G.); Department of Orthopedic Surgery, University of Florida, Gainesville, Florida (P.G. and D.B.); Department of Neurology, University of Florida, Gainesville, Florida (K.H. and T.G.M.); Department of Anesthesiology, Duke University, Durham, North Carolina (D.L.M.); Department of Neurology, Drexel University, Philadelphia, Pennsylvania (D.J.L.); and Department of Neuroscience, University of Florida, Gainesville, Florida (C.L.)
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
48
|
Graham J, Hutchinson C, Muir L. Automatic generation of statistical pose and shape models for articulated joints. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:372-383. [PMID: 24132008 DOI: 10.1109/tmi.2013.2285503] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.
Collapse
|
49
|
Perdue KL, Diamond SG. T1 magnetic resonance imaging head segmentation for diffuse optical tomography and electroencephalography. JOURNAL OF BIOMEDICAL OPTICS 2014; 19:026011. [PMID: 24531143 PMCID: PMC3924797 DOI: 10.1117/1.jbo.19.2.026011] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Accepted: 01/16/2014] [Indexed: 05/12/2023]
Abstract
Accurate segmentation of structural magnetic resonance images is critical for creating subject-specific forward models for functional neuroimaging source localization. In this work, we present an innovative segmentation algorithm that generates accurate head tissue layer thicknesses that are needed for diffuse optical tomography (DOT) data analysis. The presented algorithm is compared against other publicly available head segmentation methods. The proposed algorithm has a root mean square scalp thickness error of 1.60 mm, skull thickness error of 1.96 mm, and summed scalp and skull error of 1.49 mm. We also introduce a segmentation evaluation metric that evaluates the accuracy of tissue layer thicknesses in regions of the head where optodes are typically placed. The presented segmentation algorithm and evaluation metric are tools for improving the localization accuracy of neuroimaging with DOT, and also multimodal neuroimaging such as combined electroencephalography and DOT.
Collapse
Affiliation(s)
- Katherine L. Perdue
- Dartmouth College, Thayer School of Engineering, 14 Engineering Drive, Hanover, New Hampshire 03755
- Address all correspondence to: Katherine L. Perdue, E-mail:
| | - Solomon G. Diamond
- Dartmouth College, Thayer School of Engineering, 14 Engineering Drive, Hanover, New Hampshire 03755
| |
Collapse
|
50
|
An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: a proof of concept. Radiother Oncol 2014; 110:309-16. [PMID: 24486116 DOI: 10.1016/j.radonc.2013.12.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Revised: 12/16/2013] [Accepted: 12/28/2013] [Indexed: 11/21/2022]
Abstract
PURPOSE In radiotherapy, PET images can be used to guide the delivery of selectively escalated doses to biologically relevant tumour subvolumes. Validation of PET for such applications requires demonstration of spatial coincidence between PET tracer uptake pattern and the histopathologically confirmed target. This study introduces a novel approach to histopathological validation of PET image segmentation for radiotherapy guidance. METHODS AND MATERIALS Sequential tissue sections from surgically excised whole-tumour specimens were used to acquire full 3D-sets of both histopathological images (microscopy) and PET tracer distribution images (autoradiography). After these datasets were accurately registered, a full 3D autoradiographic distribution of PET tracer was reconstructed and used to obtain synthetic PET images (sPET) by simulating the image deterioration induced by processes involved in PET image formation. To illustrate the method, sPET images were used in this study to investigate spatial coincidence between high FDG uptake areas and the distribution of viable tissue in two small animal tumour models. RESULTS The reconstructed 3D autoradiographic distribution of the PET tracer was spatially coherent, as indicated by the high average value of the normalised pixel-by-pixel correlation of intensities between successive slices (0.84 ± 0.05 and 0.94 ± 0.02). The loss of detail in the sPET images versus the 3D autoradiography was significant as indicated by Dice coefficient values corresponding to the two tumours (0 and 0.1 at 70% threshold). The maximum overlap between the FDG segmented volumes and the extent of the viable tissue as indicated by Dice coefficient values, was 0.8 for one tumour (for the image thresholded at 22% of max intensity) and 0.88 for the other (threshold of 14% of max intensity). CONCLUSION It was demonstrated that the use of synthetic PET images for histopathological validation allows for bypassing a technically challenging and error-prone step of registering non-invasive PET images with histopathology.
Collapse
|