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Epstein ML, Obara PR, Chen Y, Liu J, Zarshenas A, Makkinejad N, Dachman AH, Suzuki K. Quantitative radiology: automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing. Quant Imaging Med Surg 2015; 5:673-84. [PMID: 26682137 DOI: 10.3978/j.issn.2223-4292.2015.10.06] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our purpose was to develop a computerized measurement of polyp volume in computed tomography colonography (CTC). METHODS We developed a 3D automated scheme for measuring polyp volume at CTC. Our scheme consisted of segmentation of colon wall to confine polyp segmentation to the colon wall, extraction of a highly polyp-like seed region based on the Hessian matrix, a 3D volume growing technique under the minimum surface expansion criterion for segmentation of polyps, and sub-voxel refinement and surface smoothing for obtaining a smooth polyp surface. Our database consisted of 30 polyp views (15 polyps) in CTC scans from 13 patients. Each patient was scanned in the supine and prone positions. Polyp sizes measured in optical colonoscopy (OC) ranged from 6-18 mm with a mean of 10 mm. A radiologist outlined polyps in each slice and calculated volumes by summation of volumes in each slice. The measurement study was repeated 3 times at least 1 week apart for minimizing a memory effect bias. We used the mean volume of the three studies as "gold standard". RESULTS Our measurement scheme yielded a mean polyp volume of 0.38 cc (range, 0.15-1.24 cc), whereas a mean "gold standard" manual volume was 0.40 cc (range, 0.15-1.08 cc). The "gold-standard" manual and computer volumetric reached excellent agreement (intra-class correlation coefficient =0.80), with no statistically significant difference [P (F≤f) =0.42]. CONCLUSIONS We developed an automated scheme for measuring polyp volume at CTC based on Hessian matrix-based shape extraction and volume growing. Polyp volumes obtained by our automated scheme agreed excellently with "gold standard" manual volumes. Our fully automated scheme can efficiently provide accurate polyp volumes for radiologists; thus, it would help radiologists improve the accuracy and efficiency of polyp volume measurements in CTC.
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Affiliation(s)
- Mark L Epstein
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Piotr R Obara
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Yisong Chen
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Junchi Liu
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Amin Zarshenas
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Nazanin Makkinejad
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Abraham H Dachman
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Kenji Suzuki
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
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Martínez F, Ruano J, Gómez M, Romero E. Estimating the size of polyps during actual endoscopy procedures using a spatio-temporal characterization. Comput Med Imaging Graph 2015; 43:130-6. [PMID: 25670148 DOI: 10.1016/j.compmedimag.2015.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Revised: 12/25/2014] [Accepted: 01/09/2015] [Indexed: 11/28/2022]
Abstract
Colorectal cancer usually appears in polyps developed from the mucosa. Carcinoma is frequently found in those polyps larger than 10mm and therefore only this kind of polyps is sent for pathology examination. In consequence, accurate estimation of a polyp size determines the surveillance interval after polypectomy. The follow up consists in a periodic colonoscopy whose frequency depends on the estimation of the size polyp. Typically, this polyp measure is achieved by examining the lesion with a calibrated endoscopy tool. However, measurement is very challenging because it must be performed during a procedure subjected to a complex mix of noise sources, namely anatomical variability, drastic illumination changes and abrupt camera movements. This work introduces a semi-automatic method that estimates a polyp size by propagating an initial manual delineation in a single frame to the whole video sequence using a spatio-temporal characterization of the lesion, during a routine endoscopic examination. The proposed approach achieved a Dice Score of 0.7 in real endoscopy video-sequences, when comparing with an expert. In addition, the method obtained a root mean square error (RMSE) of 0.87mm in videos artificially captured in a cylindric structure with spheres of known size that simulated the polyps. Finally, in real endoscopy sequences, the diameter estimation was compared with measures obtained by a group of four experts with similar experience, obtaining a RMSE of 4.7mm for a set of polyps measuring from 5 to 20mm. An ANOVA test performed for the five groups of measurements (four experts and the method) showed no significant differences (p<0.01).
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Affiliation(s)
- Fabio Martínez
- Computer Imaging and Medical Applications Laboratory-CIM@Lab, Universidad Nacional de Colombia, Carrera 30 45-03-Ciudad Universitaria Facultad de Medicina-Edificio 471, Bogotá D.C., Colombia
| | - Josué Ruano
- Computer Imaging and Medical Applications Laboratory-CIM@Lab, Universidad Nacional de Colombia, Carrera 30 45-03-Ciudad Universitaria Facultad de Medicina-Edificio 471, Bogotá D.C., Colombia
| | - Martín Gómez
- Computer Imaging and Medical Applications Laboratory-CIM@Lab, Universidad Nacional de Colombia, Carrera 30 45-03-Ciudad Universitaria Facultad de Medicina-Edificio 471, Bogotá D.C., Colombia
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory-CIM@Lab, Universidad Nacional de Colombia, Carrera 30 45-03-Ciudad Universitaria Facultad de Medicina-Edificio 471, Bogotá D.C., Colombia.
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Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, Bendtsen C, Velazquez ER, Dekker A, Aerts H, Lambin P, Li X, Tian J, Gatenby RA, Gillies RJ. Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach. PATTERN RECOGNITION 2013; 46:692-702. [PMID: 23459617 PMCID: PMC3580869 DOI: 10.1016/j.patcog.2012.10.005] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A single click ensemble segmentation (SCES) approach based on an existing "Click&Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.
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Affiliation(s)
- Yuhua Gu
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
| | - Virendra Kumar
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida 33620. USA
| | - Dmitry B Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida 33620. USA
| | - Ching-Yen Li
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida 33620. USA
| | - René Korn
- Definiens AG, Trappentreustraße 1, 80339 München, Germany
| | - Claus Bendtsen
- DECS, AstraZeneca, 50S27 Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK
| | | | - Andre Dekker
- Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, Netherlands
| | - Hugo Aerts
- Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, Netherlands
| | - Philippe Lambin
- Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, Netherlands
| | - Xiuli Li
- Medical Image Processing Group, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jie Tian
- Medical Image Processing Group, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Robert A Gatenby
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
| | - Robert J Gillies
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
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Protrusion method for automated estimation of polyp size on CT colonography. AJR Am J Roentgenol 2008; 190:1279-85. [PMID: 18430844 DOI: 10.2214/ajr.07.2865] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The purpose of this study was to assess the accuracy and measurement variability of automated lesion measurement on CT colonography in comparison with manual 2D and 3D techniques under varying scanning conditions. MATERIALS AND METHODS The study included phantoms (23 phantom objects) and patients (16 polyps). Measurement with sliding calipers served as the reference for the phantom data. The mean of two independent colonoscopic measurements was the reference for the polyps. The automated measurement was developed for a computer-aided detection scheme, and the size of any detected object was obtained from measurement of its largest diameter. The automated measurement was compared with manual 2D and 3D measurements by two experienced observers. RESULTS For phantom data, the measurement variability of the automated method was significantly less than that of the two observers (p < 0.05), except for the 3D measurement by observer 1, as follows: automated, 0.86 mm; observer 1, 1.76 mm (2D), 0.96 (3D); observer 2, 1.34 mm (2D), 1.45 mm (3D). The variability of the automated method did not differ significantly from that of manual methods in measurement with patient data. The automated method had a systematic error for phantom data (1.9 mm). CONCLUSION For phantoms, the automated method has less measurement variability than manual 2D and 3D techniques. For true polyps, the measurement variability of the automated method is comparable with that of manual methods. The automated method does not suffer from intraobserver variability. Because systematic error can be calibrated, automated size measurement may contribute to a practical evaluation strategy.
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Wang S, Yao J, Summers RM. Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction. Med Phys 2008; 35:1377-86. [PMID: 18491532 PMCID: PMC2669284 DOI: 10.1118/1.2870218] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2007] [Revised: 01/24/2008] [Accepted: 01/25/2008] [Indexed: 01/15/2023] Open
Abstract
Computer-aided detection (CAD) has been shown to be feasible for polyp detection on computed tomography (CT) scans. After initial detection, the dataset of colonic polyp candidates has large-scale and high dimensional characteristics. In this article, we propose a nonlinear dimensionality reduction method based on diffusion map and locally linear embedding (DMLLE) for large-scale datasets. By selecting partial data as landmarks, we first map these points into a low dimensional embedding space using the diffusion map. The embedded landmarks can be viewed as a skeleton of whole data in the low dimensional space. Then by using the locally linear embedding algorithm, nonlandmark samples are mapped into the same low dimensional space according to their nearest landmark samples. The local geometry is preserved in both the original high dimensional space and the embedding space. In addition, DMLLE provides a faithful representation of the original high dimensional data at coarse and fine scales. Thus, it can capture the intrinsic distance relationship between samples and reduce the influence of noisy features, two aspects that are crucial to achieving high classifier performance. We applied the proposed DMLLE method to a colonic polyp dataset of 175 269 polyp candidates with 155 features. Visual inspection shows that true polyps with similar shapes are mapped to close vicinity in the low dimensional space. We compared the performance of a support vector machine (SVM) classifier in the low dimensional embedding space with that in the original high dimensional space, SVM with principal component analysis dimensionality reduction and SVM committee using feature selection technology. Free-response receiver operating characteristic analysis shows that by using our DMLLE dimensionality reduction method, SVM achieves higher sensitivity with a lower false positive rate compared with other methods. For 6-9 mm polyps (193 true polyps contained in test set), when the number of false positives per patient is 9, SVM with DMLLE improves the average sensitivity from 70% to 83% compared with that of an SVM committee classifier which is a state-of-the-art method for colonic polyp detection (p<0.001).
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Affiliation(s)
- Shijun Wang
- Diagnostic Radiology Department, National Institutes of Health Clinical Center, Building 10, Bethesda, Maryland 20892-1182, USA
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