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Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.
Xu Z, Burke RP, Lee CP, Baucom RB, Poulose BK, Abramson RG, Landman BA
(2015) Med Image Anal 24: 18-27
MeSH Terms: Algorithms, Humans, Liver Neoplasms, Machine Learning, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Radiography, Abdominal, Reproducibility of Results, Sensitivity and Specificity, Tomography, X-Ray Computed
Show Abstract · Added February 15, 2016
Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.
Copyright © 2015 Elsevier B.V. All rights reserved.
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11 MeSH Terms
Automatic localization of cochlear implant electrodes in CT.
Zhao Y, Dawant BM, Labadie RF, Noble JH
(2014) Med Image Comput Comput Assist Interv 17: 331-8
MeSH Terms: Algorithms, Artificial Intelligence, Cochlear Implantation, Cochlear Implants, Humans, Pattern Recognition, Automated, Prosthesis Fitting, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Radiography, Interventional, Reproducibility of Results, Sensitivity and Specificity, Tomography, X-Ray Computed, Treatment Outcome
Show Abstract · Added February 19, 2015
Cochlear Implants (CI) are surgically implanted neural prosthetic devices used to treat severe-to-profound hearing loss. Recent studies have suggested that hearing outcomes with CIs are correlated with the location where individual electrodes in the implanted electrode array are placed, but techniques proposed for determining electrode location have been too coarse and labor intensive to permit detailed analysis on large numbers of datasets. In this paper, we present a fully automatic snake-based method for accurately localizing CI electrodes in clinical post-implantation CTs. Our results show that average electrode localization errors with the method are 0.21 millimeters. These results indicate that our method could be used in future large scale studies to analyze the relationship between electrode position and hearing outcome, which potentially could lead to technological advances that improve hearing outcomes with CIs.
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14 MeSH Terms
Automatic segmentation of intra-cochlear anatomy in post-implantation CT of unilateral cochlear implant recipients.
Reda FA, McRackan TR, Labadie RF, Dawant BM, Noble JH
(2014) Med Image Anal 18: 605-15
MeSH Terms: Algorithms, Artificial Intelligence, Cochlea, Cochlear Implantation, Humans, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Sensitivity and Specificity, Surgery, Computer-Assisted, Tomography, X-Ray Computed, Treatment Outcome
Show Abstract · Added February 19, 2015
A cochlear implant (CI) is a neural prosthetic device that restores hearing by directly stimulating the auditory nerve using an electrode array that is implanted in the cochlea. In CI surgery, the surgeon accesses the cochlea and makes an opening where he/she inserts the electrode array blind to internal structures of the cochlea. Because of this, the final position of the electrode array relative to intra-cochlear anatomy is generally unknown. We have recently developed an approach for determining electrode array position relative to intra-cochlear anatomy using a pre- and a post-implantation CT. The approach is to segment the intra-cochlear anatomy in the pre-implantation CT, localize the electrodes in the post-implantation CT, and register the two CTs to determine relative electrode array position information. Currently, we are using this approach to develop a CI programming technique that uses patient-specific spatial information to create patient-customized sound processing strategies. However, this technique cannot be used for many CI users because it requires a pre-implantation CT that is not always acquired prior to implantation. In this study, we propose a method for automatic segmentation of intra-cochlear anatomy in post-implantation CT of unilateral recipients, thus eliminating the need for pre-implantation CTs in this population. The method is to segment the intra-cochlear anatomy in the implanted ear using information extracted from the normal contralateral ear and to exploit the intra-subject symmetry in cochlear anatomy across ears. To validate our method, we performed experiments on 30 ears for which both a pre- and a post-implantation CT are available. The mean and the maximum segmentation errors are 0.224 and 0.734mm, respectively. These results indicate that our automatic segmentation method is accurate enough for developing patient-customized CI sound processing strategies for unilateral CI recipients using a post-implantation CT alone.
Copyright © 2014 Elsevier B.V. All rights reserved.
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13 MeSH Terms
In-line phase shift tomosynthesis.
Hammonds JC, Price RR, Pickens DR, Donnelly EF
(2013) Med Phys 40: 081911
MeSH Terms: Feasibility Studies, Imaging, Three-Dimensional, Phantoms, Imaging, Radiographic Image Enhancement
Show Abstract · Added February 28, 2014
PURPOSE - The purpose of this work is to (1) demonstrate laboratory measurements of phase shift images derived from in-line phase-contrast radiographs using the attenuation-partition based algorithm (APBA) of Yan et al. [Opt. Express 18(15), 16074-16089 (2010)], (2) verify that the APBA reconstructed images obey the linearity principle, and (3) reconstruct tomosynthesis phase shift images from a collection of angularly sampled planar phase shift images.
METHODS - An unmodified, commercially available cabinet x-ray system (Faxitron LX-60) was used in this experiment. This system contains a tungsten anode x-ray tube with a nominal focal spot size of 10 μm. The digital detector uses CsI∕CMOS with a pixel size of 50×50 μm. The phantoms used consisted of one acrylic plate, two polystyrene plates, and a habanero pepper. Tomosynthesis images were reconstructed from 51 images acquired over a ±25° arc. All phase shift images were reconstructed using the APBA.
RESULTS - Image contrast derived from the planar phase shift image of an acrylic plate of uniform thickness exceeded the contrast of the traditional attenuation image by an approximate factor of two. Comparison of the planar phase shift images from a single, uniform thickness polystyrene plate with two polystyrene plates demonstrated an approximate linearity of the estimated phase shift with plate thickness (-1600 rad vs -2970 rad). Tomographic phase shift images of the habanero pepper exhibited acceptable spatial resolution and contrast comparable to the corresponding attenuation image.
CONCLUSIONS - This work demonstrated the feasibility of laboratory-based phase shift tomosynthesis and suggests that phase shift imaging could potentially provide a new imaging biomarker. Further investigation will be needed to determine if phase shift contrast will be able to provide new tissue contrast information or improved clinical performance.
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4 MeSH Terms
Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.
McKenna MT, Wang S, Nguyen TB, Burns JE, Petrick N, Summers RM
(2012) Med Image Anal 16: 1280-92
MeSH Terms: Aged, Algorithms, Artificial Intelligence, Colonic Polyps, Colonography, Computed Tomographic, Colorectal Neoplasms, Female, Humans, Intestinal Polyps, Male, Middle Aged, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Sensitivity and Specificity
Show Abstract · Added April 21, 2016
Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC.
Copyright © 2012. Published by Elsevier B.V.
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16 MeSH Terms
Phase-contrast digital tomosynthesis.
Hammonds JC, Price RR, Donnelly EF, Pickens DR
(2011) Med Phys 38: 2353-8
MeSH Terms: Algorithms, Equipment Design, Equipment Failure Analysis, Image Enhancement, Image Interpretation, Computer-Assisted, Radiographic Image Enhancement, Reproducibility of Results, Sensitivity and Specificity, Tomography, X-Ray Computed
Show Abstract · Added February 28, 2014
PURPOSE - Phase-contrast (PC) edge enhancement occurs at the boundary between different tissues and is an interference effect that results from the differential phase-shifts that the x-rays acquire while traversing the two tissues. While observable in planar phase-contrast radiographs, the impact of digital tomosynthesis on this edge enhancement effect has not been previously reported. The purpose of this work is to demonstrate: (1) that phase-contrast digital tomosynthesis (PC-DTS) is possible with a conventional x-ray source, (2) that the reconstructed tomosynthesis images demonstrate and retain edge enhancement as compared to planar phase-contrast radiographs and (3) tomosynthesis improves object contrast by reducing the effects of superimposed structures.
METHODS - An unmodified, commercially available cabinet x-ray system (Faxitron LX-60) was used. The system contains a tungsten anode x-ray tube that was operated at 24 kVp and 3 mAs for each PC radiographic image taken, with a nominal focal spot size of 0.010 mm. The digital detector uses CsI/CMOS with a pixel size of 0.054 mm x 0.054 mm. Objects to be imaged were attached to a computer-controlled rotating motor and are rotated +/- 25 degrees about a central position in one degree increments. At each increment, three phase-contrast radiographs are taken and then averaged to reduce the effect of noise. These planar images are then used to reconstruct a series of 56 longitudinal tomographic images with an image offset increment of about 0.7 mm.
RESULTS - Tomographic z-plane resolution was measured to be approximately 4 mm. When compared to planar PC images, the tomosynthesis images were shown to retain the PC boundary edge enhancement in addition to an improvement in object contrast.
CONCLUSIONS - Our work demonstrates that PC digital tomosynthesis retains the edge-enhancement observed in planar PC radiograph and further improves soft-tissue conspicuity by reducing the effects of superimposed tissue structure.
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9 MeSH Terms
Automatic segmentation of intracochlear anatomy in conventional CT.
Noble JH, Labadie RF, Majdani O, Dawant BM
(2011) IEEE Trans Biomed Eng 58: 2625-32
MeSH Terms: Algorithms, Humans, Radiographic Image Enhancement, Reproducibility of Results, Scala Tympani, Scala Vestibuli, Tomography, X-Ray Computed, X-Ray Microtomography
Show Abstract · Added April 10, 2018
Cochlear implant surgery is a procedure performed to treat profound hearing loss. Clinical results suggest that implanting the electrode in the scala tympani, one of the two principal cavities inside the cochlea, may result in better hearing restoration. Segmentation of intracochlear cavities could thus aid the surgeon to choose the point of entry and angle of approach that maximize the likelihood of successful implant insertion, which may lead to more substantial hearing restoration. However, because the membrane that separates the intracochlear cavities is too thin to be seen in conventional in vivo imaging, traditional segmentation techniques are inadequate. In this paper, we circumvent this problem by creating an active shape model with micro CT (μCT) scans of the cochlea acquired ex vivo. We then use this model to segment conventional CT scans. The model is fitted to the partial information available in the conventional scans and used to estimate the position of structures not visible in these images. Quantitative evaluation of our method, made possible by the set of μCTs, results in Dice similarity coefficients averaging 0.75. Mean and maximum surface errors average 0.21 and 0.80  mm.
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MeSH Terms
Are airflow obstruction and radiographic evidence of emphysema risk factors for lung cancer? A nested case-control study using quantitative emphysema analysis.
Maldonado F, Bartholmai BJ, Swensen SJ, Midthun DE, Decker PA, Jett JR
(2010) Chest 138: 1295-302
MeSH Terms: Adenocarcinoma, Age Distribution, Aged, Airway Obstruction, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Case-Control Studies, Comorbidity, Confidence Intervals, Evaluation Studies as Topic, Female, Humans, Imaging, Three-Dimensional, Incidence, Logistic Models, Lung Neoplasms, Male, Middle Aged, Pulmonary Emphysema, Radiographic Image Enhancement, Reference Values, Risk Assessment, Sex Distribution, Spirometry, Tomography, X-Ray Computed
Show Abstract · Added February 1, 2016
OBJECTIVES - Several studies have identified airflow obstruction as a risk factor for lung cancer independent of smoking history, but the risk associated with the presence of radiographic evidence of emphysema has not been extensively studied. We proposed to assess this risk using a quantitative volumetric CT scan analysis.
METHODS - Sixty-four cases of lung cancer were identified from a prospective cohort of 1,520 participants enrolled in a spiral CT scan lung cancer screening trial. Each case was matched to six control subjects for age, sex, and smoking history. Quantitative CT scan analysis of emphysema was performed. Spirometric measures were also conducted. Data were analyzed using conditional logistic regression making use of the 1:6 set groups of 64 cases and 377 matched control subjects.
RESULTS - Decreased FEV(1) and FEV(1)/FVC were significantly associated with a diagnosis of lung cancer with ORs of 1.15 (95% CI, 1.00-1.32; P = .046) and 1.29 (95% CI, 1.02-1.62; P = .031), respectively. The quantity of radiographic evidence of emphysema was not found to be a significant risk for lung cancer with OR of 1.042 (95% CI, 0.816-1.329; P = .743). Additionally, there was no significant association between severe emphysema and lung cancer with OR of 1.57 (95% CI, 0.73-3.37).
CONCLUSIONS - We confirm previous observations that airflow obstruction is an independent risk factor for lung cancer. The absence of a clear relationship between radiographic evidence of emphysema and lung cancer using an automated quantitative volumetric analysis may result from different population characteristics than those of prior studies, radiographic evidence of emphysema quantitation methodology, or absence of any relationship between emphysema and lung cancer risk.
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25 MeSH Terms
Minimizing grid cutoff.
Lehrer R
(2010) Radiol Technol 81: 280-1
MeSH Terms: Equipment Design, Radiographic Image Enhancement, Radiography
Added April 26, 2017
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3 MeSH Terms
The loss of radiographic enhancement in primary renal cell carcinoma tumors following multitargeted receptor tyrosine kinase therapy is an additional indicator of response.
Cowey CL, Fielding JR, Rathmell WK
(2010) Urology 75: 1108-13.e1
MeSH Terms: Antineoplastic Agents, Benzenesulfonates, Carcinoma, Renal Cell, Female, Humans, Indoles, Kidney Neoplasms, Male, Middle Aged, Niacinamide, Phenylurea Compounds, Protein Kinase Inhibitors, Pyridines, Pyrroles, Radiographic Image Enhancement, Retrospective Studies, Sorafenib, Sunitinib, Treatment Outcome
Show Abstract · Added October 17, 2015
OBJECTIVES - To characterize radiographic intratumoral contrast enhancement in the primary tumor of patients with renal cell carcinoma treated with either sorafenib or sunitinib, and to compare the relationship between primary tumor response and loss of enhancement. Use of the antiangiogenic multitargeted tyrosine kinase inhibitors sorafenib and sunitinib in renal cell carcinoma often results in stabilization of tumor size based on measurement of external tumor diameter; however, internal tumor changes in enhancement have been occasionally noted.
METHODS - Thirty patients who received sunitinib or sorafenib therapy were evaluated for primary tumor response with contrast-enhanced computed tomography images before and after at least 1 cycle of treatment. Evaluation of intratumoral contrast enhancement was quantified using a workstation that allowed for three-dimensional renderings of the kidney and measurement of density in Hounsfield units (HU). The relationship between loss of intratumoral enhancement and other outcome variables was examined.
RESULTS - A loss of enhancement within the primary tumor, following therapy with tyrosine kinase inhibitors, was positively associated with primary tumor response (P = .0053). Additionally, the degree of post-treatment tumor enhancement was positively associated with tumor response to tyrosine kinase inhibition (P = .045).
CONCLUSIONS - Intratumoral changes in computed tomography enhancement after receptor tyrosine kinase inhibition correlate with primary tumor response, and may be a useful adjunct to the standard response evaluation criteria in solid tumors (RECIST criteria) in assessing response to therapy. Prospective studies evaluating antiangiogenic agents should explore intratumoral changes in contrast enhancement as part of response criteria, and examine the effect of intratumoral changes on survival-based outcomes.
Copyright 2010 Elsevier Inc. All rights reserved.
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19 MeSH Terms