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Posselt C, Avci MY, Yigitsoy M, Schuenke P, Kolbitsch C, Schaeffter T, Remmele S. Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks. J Med Imaging (Bellingham) 2024; 11:024013. [PMID: 38666039 PMCID: PMC11042016 DOI: 10.1117/1.jmi.11.2.024013] [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: 08/10/2023] [Revised: 03/01/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
Purpose To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. Approach The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI). Results The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R 2 > 0.9 ). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI. Conclusions We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.
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Affiliation(s)
- Christiane Posselt
- University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany
| | | | | | - Patrick Schuenke
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Technical University of Berlin, Department of Medical Engineering, Berlin, Germany
| | - Stefanie Remmele
- University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany
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2
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Chen Z, Gould A, Geleri DB, Balu N, Chen L, Chu B, Pimentel K, Canton G, Hatsukami TS, Yuan C. Associations of intracranial artery length and branch number on non-contrast enhanced MRA with cognitive impairment in individuals with carotid atherosclerosis. Sci Rep 2022; 12:7456. [PMID: 35524158 PMCID: PMC9076596 DOI: 10.1038/s41598-022-11418-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/25/2022] [Indexed: 11/15/2022] Open
Abstract
Developing novel risk markers for vascular contributions to cognitive impairment and dementia is important. This study aimed to extract total length, branch number and average tortuosity of intracranial distal arteries (A2, M2, P2 and more distal) from non-contrast enhanced magnetic resonance angiography (NCE-MRA) images, and explore their associations with global cognition. In 29 subjects (aged 40-90 years) with carotid atherosclerotic disease, the 3 intracranial vascular features on two NCE-MRA techniques (i.e. time of flight, TOF and simultaneous non-contrast angiography and intraplaque hemorrhage, SNAP) were extracted using a custom-developed software named iCafe. Arterial spin labeling (ASL) and phase contrast (PC) cerebral blood flow (CBF) were measured as references. Linear regression was performed to study their associations with global cognition, measured with the Montreal Cognitive Assessment (MoCA). Intracranial artery length and number of branches on NCE-MRA, ASL CBF and PC CBF were found to be positively associated with MoCA scores (P < 0.01). The associations remained significant for artery length and number of branches on NCE-MRA after adjusting for clinical covariates and white matter hyperintensity volume. Further adjustment of confounding factors of ASL CBF or PC CBF did not abolish the significant association for artery length and number of branches on TOF. Our findings suggest that intracranial vascular features, including artery length and number of branches, on NCE-MRA may be useful markers of cerebrovascular health and provide added information over conventional brain blood flow measurements in individuals with cognitive impairment.
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Affiliation(s)
- Zhensen Chen
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA.
- BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, WA, USA.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Anders Gould
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Duygu Baylam Geleri
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA
| | - Niranjan Balu
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA
- BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, WA, USA
| | - Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Baocheng Chu
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA
- BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, WA, USA
| | - Kristi Pimentel
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA
| | - Gador Canton
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA
| | | | - Chun Yuan
- Vascular Imaging Laboratory, Department of Radiology, University of Washington, 850 Republican Street, Box 358050, Seattle, WA, 98109, USA
- BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, WA, USA
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Ramirez GA, Rocca MA, Preziosa P, Bozzolo EP, Pagani E, Canti V, Moiola L, Rovere-Querini P, Manfredi AA, Filippi M. Quantitative MRI adds to neuropsychiatric lupus diagnostics. Rheumatology (Oxford) 2021; 60:3278-3288. [PMID: 33367829 DOI: 10.1093/rheumatology/keaa779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 11/02/2020] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE Attributing neuropsychiatric manifestations to SLE is often challenging. Brain white matter lesions are frequent in SLE at MRI, but their diagnostic role is unclear. Here, we assessed whether white matter lesions count, volume and distribution measurement can help in the diagnosis of neuropsychiatric systemic lupus erythematosus (NPSLE). METHODS Brain dual-echo and 3D T1-weighted sequences were acquired from 32 patients with SLE and 32 healthy controls with a 3 T-scanner and employed to derive T2-hyperintense lesion volume (T2LV), number (T2LN) and probability maps (LPM) using a semi-automatic local thresholding segmentation technique. NPSLE was classified as per the ACR nomenclature, the Italian Society for Rheumatology algorithm and by clinical impression. Clinical descriptors including the SLE International Collaborating Clinics/ACR damage index (SDI) were also recorded. RESULTS Higher T2LV were observed in SLE vs healthy controls (P < 0.001) and in NPSLE vs other SLE (P =0.006). Patients with NPSLE also had higher T2LN (P =0.003) compared with other SLE. In SLE, T2LPM revealed a high prevalence of lesions in the splenium of the corpus callosum, right superior longitudinal fasciculus and right corona radiata. T2LV and T2LN correlated with SLE duration (rho = 0.606; P <0.001 and rho = 0.483; P =0.005, respectively) and age (rho = 0.478; P =0.006 and rho = 0.362; P = 0.042, respectively). T2LV also correlated with SDI (rho = 0.352; P =0.048). SLE patients with fatigue had lower T2LN (P =0.038) compared with patients without fatigue. Thresholds of T2LV ≥ 0.423 cm3 or of T2LN ≥ 12 were associated with definite NPSLE and improved the classification of patients with possible NPSLE per clinical impression. CONCLUSION Brain white matter lesions (WML) quantitation adds to NPSLE diagnostics.
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Affiliation(s)
- Giuseppe A Ramirez
- Università Vita-Salute San Raffaele.,Unit of Immunology, Rheumatology, Allergy and Rare Diseases.,Division of Immunogy, Transplantation & Infectious Diseases
| | | | | | | | | | | | | | - Patrizia Rovere-Querini
- Università Vita-Salute San Raffaele.,Division of Immunogy, Transplantation & Infectious Diseases
| | - Angelo A Manfredi
- Università Vita-Salute San Raffaele.,Unit of Immunology, Rheumatology, Allergy and Rare Diseases.,Division of Immunogy, Transplantation & Infectious Diseases
| | - Massimo Filippi
- Università Vita-Salute San Raffaele.,Neuroimaging Research Unit.,Neurology Unit.,Neurophysiology Unit, IRCCS Ospedale San Raffaele, Milan, Italy
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4
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Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
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Sarbu MI, Sarbu N. Fulminant Brain Atrophy and Vasculitis on Vessel-Wall Imaging in Neuropsychiatric Lupus: Case Report and Literature Review. Arch Rheumatol 2020; 35:443-448. [PMID: 33458670 PMCID: PMC7788661 DOI: 10.46497/archrheumatol.2020.7544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Accepted: 07/16/2019] [Indexed: 01/21/2023] Open
Abstract
Brain atrophy, typically slowly progressive, is a hallmark of neuropsychiatric (NP) systemic lupus erythematosus (SLE). In this article, we report a case of a young female patient with previously diagnosed SLE, without known NPSLE, with abrupt neurological deterioration and rapidly progressive diffuse atrophy in a few months. A comprehensive diagnostic work-up and follow-up magnetic resonance imaging (MRI), including high-resolution advanced vessel-wall sequences, revealed underlying cerebral vasculitis. The novelty factors that the present report brings are the rapid progressive atrophy demonstrated on follow-up MRI in a patient with SLE, and the depiction of an underlying vasculitis on specific vessel-wall MRI techniques. We also reviewed the literature and discussed the main current applications of vessel-wall MRI sequences. The aim of the report is to recognize this dramatic form of presentation of NPSLE and the utility of the new MRI techniques for the diagnosis.
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Affiliation(s)
- Mihaela Ionela Sarbu
- Saint Pierre University Hospital; Erasme Hospital, Rheumatology, Brussels, Belgium
| | - Nicolae Sarbu
- Department of Neuroradiology, Erasme Hospital, Brussels, Belgium.,Department of Neuroradiology, Faculty of Medicine, "Dunarea de Jos" University, Galati, Romania
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6
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Sarbu N, Lolli V, Smirniotopoulos JG. Magnetic resonance imaging in myelopathy: a pictorial review. Clin Imaging 2019; 57:56-68. [DOI: 10.1016/j.clinimag.2019.05.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 04/30/2019] [Accepted: 05/13/2019] [Indexed: 11/26/2022]
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7
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Ghribi O, Maalej A, Sellami L, Ben Slima M, Maalej MA, Ben Mahfoudh K, Dammak M, Mhiri C, Ben Hamida A. Advanced methodology for multiple sclerosis lesion exploring: Towards a computer aided diagnosis system. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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8
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Diniz PHB, Valente TLA, Diniz JOB, Silva AC, Gattass M, Ventura N, Muniz BC, Gasparetto EL. Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 167:49-63. [PMID: 29706405 DOI: 10.1016/j.cmpb.2018.04.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 02/12/2018] [Accepted: 04/17/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND OBJECTIVE White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. METHODS The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. RESULTS The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. CONCLUSIONS It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions.
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Affiliation(s)
- Pedro Henrique Bandeira Diniz
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - Thales Levi Azevedo Valente
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - João Otávio Bandeira Diniz
- Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil.
| | - Aristófanes Corrêa Silva
- Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil.
| | - Nina Ventura
- Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil.
| | - Bernardo Carvalho Muniz
- Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil.
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9
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Kalinowska-Lyszczarz A, Pawlak MA, Pietrzak A, Pawlak-Bus K, Leszczynski P, Puszczewicz M, Paprzycki W, Kozubski W, Michalak S. Distinct regional brain atrophy pattern in multiple sclerosis and neuropsychiatric systemic lupus erythematosus patients. Lupus 2018; 27:1624-1635. [PMID: 29950159 DOI: 10.1177/0961203318781004] [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] [Indexed: 11/15/2022]
Abstract
Differentiation of systemic lupus erythematosus (SLE) from multiple sclerosis (MS) can be challenging, especially when neuropsychiatric (NP) symptoms are accompanied by white matter lesions in the brain. Given the lack of discriminative power of currently applied tools for their differentiation, there is an unmet need for other measures that can aid in distinguishing between the two autoimmune disorders. In this study we aimed at exploring whether brain atrophy measures could serve as markers differentiating MS and SLE. Thirty-seven relapsing-remitting MS and 38 SLE patients with nervous system manifestations, matched according to age and disease duration, underwent 1.5 Tesla magnetic resonance imaging (MRI), including volumetric sequences, and clinical assessment. Voxelwise analysis was performed using ANTS-SyN elastic registration protocol, FSL Randomise and Gamma methods. Cortical and subcortical segmentation was performed with Freesurfer 5.3 pipeline using T1-weighted MPRAGE sequence data. Using MRI volumetric markers of general and subcortical gray matter atrophy and clinical variables, we built a stepwise multivariable logistic diagnostic model to identify MRI parameters that best differentiate MS and SLE patients. We found that the best volumetric predictors to distinguish them were: fourth ventricle volume (sensitivity 0.86, specificity 0.57, area under the curve, AUC 0.77), posterior corpus callosum (sensitivity 0.81, specificity 0.57, AUC 0.68), and third ventricle to thalamus ratio (sensitivity 0.42, specificity 0.84, AUC 0.65). The same classifiers were identified in a subgroup analysis that included patients with a short disease duration. In MS brain atrophy and lesion load correlated with clinical disability, while in SLE age was the main determinant of brain volume. This study proposes new imaging parameters for differential diagnosis of MS and SLE with central nervous system involvement. We show there is a different pattern of atrophy in MS and SLE, and the key structural volumes that are differentially affected include fourth ventricle and posterior section of corpus callosum, followed by third ventricle to thalamus ratio. Different correlation patterns between volumetric and clinical data may suggest that while in MS atrophy is driven mainly by disease activity, in SLE it is mostly associated with age. However, these results need further replication in a larger cohort.
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Affiliation(s)
- A Kalinowska-Lyszczarz
- 1 Division of Neurochemistry and Neuropathology, Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | - M A Pawlak
- 2 Department of Neurology and Cerebrovascular Disorders, Poznan University of Medical Sciences, Poznan, Poland
| | - A Pietrzak
- 3 Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | - K Pawlak-Bus
- 4 Department of Rheumatology and Rehabilitation, Poznan University of Medical Sciences, Poznan, Poland
| | - P Leszczynski
- 4 Department of Rheumatology and Rehabilitation, Poznan University of Medical Sciences, Poznan, Poland
| | - M Puszczewicz
- 5 Department of Rheumatology and Internal Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | - W Paprzycki
- 6 Department of Neuroradiology, Poznan University of Medical Sciences, Poznan, Poland
| | - W Kozubski
- 3 Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
| | - S Michalak
- 1 Division of Neurochemistry and Neuropathology, Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland
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Agarwal R, Diaz O, Lladó X, Gubern-Mérida A, Vilanova JC, Martí R. Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis. ULTRASONIC IMAGING 2018; 40:97-112. [PMID: 29182056 DOI: 10.1177/0161734617737733] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ([Formula: see text]) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of [Formula: see text] with a mean False Positive ratio [Formula: see text] has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is [Formula: see text] ([Formula: see text]). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient [Formula: see text] ([Formula: see text]) for prior and [Formula: see text] ([Formula: see text]) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.
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Affiliation(s)
- Richa Agarwal
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
| | - Oliver Diaz
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
| | - Xavier Lladó
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
| | - Albert Gubern-Mérida
- 2 Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Robert Martí
- 1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain
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Valverde S, Cabezas M, Roura E, González-Villà S, Pareto D, Vilanova JC, Ramió-Torrentà L, Rovira À, Oliver A, Lladó X. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Neuroimage 2017; 155:159-168. [DOI: 10.1016/j.neuroimage.2017.04.034] [Citation(s) in RCA: 206] [Impact Index Per Article: 29.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 03/12/2017] [Accepted: 04/14/2017] [Indexed: 12/30/2022] Open
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