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Djahnine A, Lazarus C, Lederlin M, Mulé S, Wiemker R, Si-Mohamed S, Jupin-Delevaux E, Nempont O, Skandarani Y, De Craene M, Goubalan S, Raynaud C, Belkouchi Y, Afia AB, Fabre C, Ferretti G, De Margerie C, Berge P, Liberge R, Elbaz N, Blain M, Brillet PY, Chassagnon G, Cadour F, Caramella C, Hajjam ME, Boussouar S, Hadchiti J, Fablet X, Khalil A, Talbot H, Luciani A, Lassau N, Boussel L. Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution. Diagn Interv Imaging 2024; 105:97-103. [PMID: 38261553 DOI: 10.1016/j.diii.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 01/25/2024]
Abstract
PURPOSE The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations. MATERIALS AND METHODS Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio. RESULTS Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850-0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810-0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668-0.760) and of 0.723 (95% CI: 0.668-0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set. CONCLUSION This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.
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Affiliation(s)
- Aissam Djahnine
- Philips Research France, 92150 Suresnes, France; CREATIS, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France.
| | | | | | - Sébastien Mulé
- Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France
| | | | - Salim Si-Mohamed
- Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France
| | | | | | | | | | | | | | - Younes Belkouchi
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France
| | - Amira Ben Afia
- Department of Radiology, APHP Nord, Hôpital Bichat, 75018 Paris, France
| | - Clement Fabre
- Department of Radiology, Centre Hospitalier de Laval, 53000 Laval, France
| | - Gilbert Ferretti
- Universite Grenobles Alpes, Service de Radiologie et Imagerie Médicale, CHU Grenoble-Alpes, 38000 Grenoble, France
| | - Constance De Margerie
- Université Paris Cité, 75006 Paris, France, Department of Radiology, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, 75010 Paris, France
| | - Pierre Berge
- Department of Radiology, CHU Angers, 49000 Angers, France
| | - Renan Liberge
- Department of Radiology, CHU Nantes, 44000 Nantes, France
| | - Nicolas Elbaz
- Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
| | - Maxime Blain
- Department of Radiology, Hopital Henri Mondor, AP-HP, 94000 Créteil, France
| | - Pierre-Yves Brillet
- Department of Radiology, Hôpital Avicenne, Paris 13 University, 93000 Bobigny, France
| | - Guillaume Chassagnon
- Department of Radiology, Hopital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Farah Cadour
- APHM, Hôpital Universitaire Timone, CEMEREM, 13005 Marseille, France
| | - Caroline Caramella
- Department of Radiology, Groupe Hospitalier Paris Saint-Joseph, 75015 Paris, France
| | - Mostafa El Hajjam
- Department of Radiology, Hôpital Ambroise Paré Hospital, UMR 1179 INSERM/UVSQ, Team 3, 92100 Boulogne-Billancourt, France
| | - Samia Boussouar
- Sorbonne Université, Hôpital La Pitié-Salpêtrière, APHP, Unité d'Imagerie Cardiovasculaire et Thoracique (ICT), 75013 Paris, France
| | - Joya Hadchiti
- Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France
| | - Xavier Fablet
- Department of Radiology, CHU Rennes, 35000 Rennes, France
| | - Antoine Khalil
- Department of Radiology, APHP Nord, Hôpital Bichat, 75018 Paris, France
| | - Hugues Talbot
- OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France
| | - Alain Luciani
- Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France
| | - Nathalie Lassau
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France
| | - Loic Boussel
- CREATIS, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France; Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France
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Grosu S, Wiemker R, An C, Obmann MM, Wong E, Yee J, Yeh BM. Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography. Eur Radiol 2022; 32:7936-7945. [PMID: 35486170 DOI: 10.1007/s00330-022-08831-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 03/15/2022] [Accepted: 04/20/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner. METHODS We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better). RESULTS Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing. CONCLUSIONS Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing. KEY POINTS • Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.
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Affiliation(s)
- Sergio Grosu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA. .,Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Rafael Wiemker
- Philips Research Laboratories Hamburg, Röntgenstraße 24, 22335, Hamburg, Germany
| | - Chansik An
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Markus M Obmann
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.,Department of Radiology and Nuclear Imaging, University Hospital Basel, Petersgraben 4, CH - 4051, Basel, Switzerland
| | - Eddy Wong
- CT/AMI Clinical Science, Philips Healthcare, 100 Park Avenue, Orange Village, OH, 44122, USA
| | - Judy Yee
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th Street, Bronx, NY, 10467-2401, USA
| | - Benjamin M Yeh
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
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Trajanovski S, Mavroeidis D, Swisher CL, Gebre BG, Veeling BS, Wiemker R, Klinder T, Tahmasebi A, Regis SM, Wald C, McKee BJ, Flacke S, MacMahon H, Pien H. Towards radiologist-level cancer risk assessment in CT lung screening using deep learning. Comput Med Imaging Graph 2021; 90:101883. [PMID: 33895622 DOI: 10.1016/j.compmedimag.2021.101883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 02/08/2021] [Accepted: 02/13/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption. In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models. METHODS Three low-dose CT lung cancer screening datasets were used: National Lung Screening Trial (NLST, n = 3410), Lahey Hospital and Medical Center (LHMC, n = 3154) data, Kaggle competition data (from both stages, n = 1397 + 505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n = 132). At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image context around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets. Special care was taken to ensure there was no patient overlap between the train and validation sets. RESULTS AND CONCLUSIONS The proposed deep learning model is shown to: (a) generalize well across all three data sets, achieving AUC between 86% to 94%, with our external test-set (LHMC) being at least twice as large compared to other works; (b) have better performance than the widely accepted PanCan Risk Model, achieving 6 and 9% better AUC score in our two test sets; (c) have improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) have comparable performance to radiologists in estimating cancer risk at a patient level.
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Affiliation(s)
| | | | | | | | - Bastiaan S Veeling
- Machine Learning lab, University of Amsterdam, 1090 GH Amsterdam and, Philips Research, Eindhoven, 5656 AE, The Netherlands
| | | | | | - Amir Tahmasebi
- Philips Research North America, Cambridge, MA, 02141, USA
| | - Shawn M Regis
- Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Christoph Wald
- Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Brady J McKee
- Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | | | - Heber MacMahon
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | - Homer Pien
- Philips Research North America, Cambridge, MA, 02141, USA
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Wiemker R, Klinder T, Bergtholdt M, Meetz K, Carlsen IC, Bülow T. A radial structure tensor and its use for shape-encoding medical visualization of tubular and nodular structures. IEEE Trans Vis Comput Graph 2013; 19:353-366. [PMID: 22689078 DOI: 10.1109/tvcg.2012.136] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The concept of curvature and shape-based rendering is beneficial for medical visualization of CT and MRI image volumes. Color-coding of local shape properties derived from the analysis of the local Hessian can implicitly highlight tubular structures such as vessels and airways, and guide the attention to potentially malignant nodular structures such as tumors, enlarged lymph nodes, or aneurysms. For some clinical applications, however, the evaluation of the Hessian matrix does not yield satisfactory renderings, in particular for hollow structures such as airways, and densely embedded low contrast structures such as lymph nodes. Therefore, as a complement to Hessian-based shape-encoding rendering, this paper introduces a combination of an efficient sparse radial gradient sampling scheme in conjunction with a novel representation, the radial structure tensor (RST). As an extension of the well-known general structure tensor, which has only positive definite eigenvalues, the radial structure tensor correlates position and direction of the gradient vectors in a local neighborhood, and thus yields positive and negative eigenvalues which can be used to discriminate between different shapes. As Hessian-based rendering, also RST-based rendering is ideally suited for GPU implementation. Feedback from clinicians indicates that shape-encoding rendering can be an effective image navigation tool to aid diagnostic workflow and quality assurance.
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Lo P, van Ginneken B, Reinhardt JM, Yavarna T, de Jong PA, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijańska A, Bauer C, Beichel R, Mendoza CS, Wiemker R, Lee J, Reeves AP, Born S, Weinheimer O, van Rikxoort EM, Tschirren J, Mori K, Odry B, Naidich DP, Hartmann I, Hoffman EA, Prokop M, Pedersen JH, de Bruijne M. Extraction of airways from CT (EXACT'09). IEEE Trans Med Imaging 2012; 31:2093-2107. [PMID: 22855226 DOI: 10.1109/tmi.2012.2209674] [Citation(s) in RCA: 97] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
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Wiemker R, Dharaiya ED, Bülow T. Informatics in Radiology: Hesse Rendering for Computer-aided Visualization and Analysis of Anomalies at Chest CT and Breast MR Imaging. Radiographics 2012; 32:289-304. [DOI: 10.1148/rg.321105076] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Apostolova I, Wiemker R, Paulus T, Kabus S, Dreilich T, van den Hoff J, Plotkin M, Mester J, Brenner W, Buchert R, Klutmann S. Combined correction of recovery effect and motion blur for SUV quantification of solitary pulmonary nodules in FDG PET/CT. Eur Radiol 2010; 20:1868-77. [DOI: 10.1007/s00330-010-1747-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2009] [Revised: 01/16/2010] [Accepted: 01/24/2010] [Indexed: 11/25/2022]
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Bangard C, Rösgen S, Wahba R, Hellmich M, Wiemker R, Fischer JH, Stippel DL, Lackner KJ. Perkutane intrahepatische RF Ablation im Schweinemodell: erste Ergebnisse mit der Rita XLi Sonde. ROFO-FORTSCHR RONTG 2010. [DOI: 10.1055/s-0030-1252775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Bangard C, Rösgen S, Wahba R, Hellmich M, Wiemker R, Fischer JH, Stippel DL, Lackner KJ. Monopolare RFA in Schweinelebern: Methode zur in vivo Bestimmung der Asymmetrie von Nekrosezonen im Verhältnis zur Applikatorschaftachse. ROFO-FORTSCHR RONTG 2010. [DOI: 10.1055/s-0030-1252797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Wiemker R, Paulus T, Kabus S, Bülow T, Apostolova I, Buchert R, Klutmann S. Combined motion blur and partial volume correction for computer aided diagnosis of pulmonary nodules in PET/CT. Int J Comput Assist Radiol Surg 2008. [DOI: 10.1007/s11548-008-0212-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wiemker R, de Hoop B, Kabus S, Gietema H, Opfer R, Dharaiya E. Performance study of a globally elastic locally rigid matching algorithm for follow-up chest CT. ACTA ACUST UNITED AC 2008. [DOI: 10.1117/12.765166] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Romano V, Hein PA, Rogalla P, Wiemker R, Meyer H, Hamm B. Einfluss einer drastischen Dosisverringerung auf die Performance zweier CAD-Systeme: Automatisierte Lungenrundherd-Detektion in der MSCT in Ultra-Niedrig-Dosis- vs. Standard-Dosis-Technik. ROFO-FORTSCHR RONTG 2007. [DOI: 10.1055/s-2007-977381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Wiemker R, Bülow T, Blaffert T. Unsupervised extraction of the pulmonary interlobar fissures from high resolution thoracic CT data. ACTA ACUST UNITED AC 2005. [DOI: 10.1016/j.ics.2005.03.130] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wiemker R, Rogalla P, Blaffert T, Sifri D, Hay O, Shah E, Truyen R, Fleiter T. Aspects of computer-aided detection (CAD) and volumetry of pulmonary nodules using multislice CT. Br J Radiol 2005; 78 Spec No 1:S46-56. [PMID: 15917446 DOI: 10.1259/bjr/30281702] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
With the superb spatial resolution of modern multislice CT scanners and their ability to complete a thoracic scan within one breath-hold, software algorithms for computer-aided detection (CAD) of pulmonary nodules are now reaching high sensitivity levels at moderate false positive rates. A number of pilot studies have shown that CAD modules can successfully find overlooked pulmonary nodules and serve as a powerful tool for diagnostic quality assurance. Equally important are tools for fast and accurate three-dimensional volume measurement of detected nodules. These allow monitoring of nodule growth between follow-up examinations for differential diagnosis and response to oncological therapy. Owing to decreasing partial volume effect, nodule volumetry is more accurate with high resolution CT data. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow-up examinations. Fast and automated growth rate monitoring with only few reader interactions also adds to diagnostic quality assurance.
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Affiliation(s)
- R Wiemker
- Philips Research Laboratories Hamburg, Germany
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Dippel S, Stahl M, Wiemker R, Blaffert T. Multiscale contrast enhancement for radiographies: Laplacian Pyramid versus fast wavelet transform. IEEE Trans Med Imaging 2002; 21:343-353. [PMID: 12022622 DOI: 10.1109/tmi.2002.1000258] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.
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Affiliation(s)
- Sabine Dippel
- Philips Research Laboratories, Division Technical Systems, Hamburg, Germany.
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Wiemker R. An iterative spectral-spatial Bayesian labeling approach for unsupervised robust change detection on remotely sensed multispectral imagery. Computer Analysis of Images and Patterns 1997. [DOI: 10.1007/3-540-63460-6_126] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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