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1.  Helping patients make choices about breast reconstruction: A decision analysis approach 
Plastic and reconstructive surgery  2014;134(4):597-608.
Decision analysis can help breast reconstruction patients and their surgeons to methodically evaluate clinical alternatives and make hard decisions. The purpose of this paper is to help plastic surgeons guide patients in making decisions though a case study in breast reconstruction. By making good decisions, patient outcomes may be improved. This paper aims to illustrate decision analysis techniques from the patient perspective with an emphasis on her values and preferences. We introduce normative decision-making through a fictional breast reconstruction patient and systematically build the decision basis to help her make a good decision. We broadly identify alternatives of breast reconstruction, propose types of outcomes that the patient should consider, discuss sources of probabilistic information and outcome values, and demonstrate how to make a good decision. The concepts presented here may be extended to other shared decision-making problems in plastic and reconstructive surgery.
In addition, we discuss how sensitivity analysis may test the robustness of the decision and how to evaluate the quality of decisions. We also present tools to help implement these concepts in practice. Finally, we examine limitations that hamper adoption of patient decision analysis in reconstructive surgery and healthcare in general. In particular, we emphasize the need for routine collection of quality of life information, out-of-pocket expense, and recovery time.
doi:10.1097/PRS.0000000000000514
PMCID: PMC4217136  PMID: 25357022
2.  Assessing Women’s Preferences and Preference Modeling for Breast Reconstruction Decision-Making 
Background
Women considering breast reconstruction must make challenging trade-offs amongst issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction.
Methods
In this study, we used multiattribute utility theory to combine multiple objectives to yield a summary value using nine different preference models. We elicited the preferences of 36 women, aged 32 or older with no history of breast cancer, for the patient-reported outcome measures of breast satisfaction, psychosocial well-being, chest well-being, abdominal well-being, and sexual wellbeing as measured by the BREAST-Q in addition to time lost to reconstruction and out-of-pocket cost. Participants ranked hypothetical breast reconstruction outcomes. We examined each multiattribute utility preference model and assessed how often each model agreed with participants’ rankings.
Results
The median amount of time required to assess preferences was 34 minutes. Agreement among the nine preference models with the participants ranged from 75.9% to 78.9%. None of the preference models performed significantly worse than the best performing risk averse multiplicative model. We hypothesize an average theoretical agreement of 94.6% for this model if participant error is included. There was a statistically significant positive correlation with more unequal distribution of weight given to the seven attributes.
Conclusions
We recommend the risk averse multiplicative model for modeling the preferences of patients considering different forms of breast reconstruction because it agreed most often with the participants in this study.
doi:10.1097/GOX.0000000000000062
PMCID: PMC4120963  PMID: 25105083
Multiattribute utility theory; breast reconstruction; BREAST-Q; patient reported outcome measures; decision analysis; decision-making; consistency; risk attitude; multiple objectives; utility
3.  Plastic Surgeon Expertise in Predicting Breast Reconstruction Outcomes for Patient Decision Analysis 
Background
Decision analysis offers a framework that may help breast cancer patients make good breast reconstruction decisions. A requirement for this type of analysis is information about the possibility of outcomes occurring in the form of probabilities. The purpose of this study was to determine if plastic surgeons are good sources of probability information, both individually and as a group, when data are limited.
Methods
Seven plastic surgeons were provided with pertinent medical information and preoperative photographs of patients, and were asked to assign probabilities to predict number of revisions, complications, and final aesthetic outcome using a questionnaire designed for the study. Logarithmic strictly proper scoring was used to evaluate the surgeons’ abilities to predict breast reconstruction outcomes. Surgeons’ responses were analyzed for calibration and confidence in their answers.
Results
As individuals, there was variation in surgeons’ ability to predict outcomes. For each prediction category, a different surgeon was more accurate. As a group, surgeons possessed knowledge of future events despite not being well calibrated in their probability assessments. Prediction accuracy for the group was up to six-fold greater than that of the best individual.
Conclusions
The use of individual plastic surgeon-elicited probability information is not encouraged unless the individual’s prediction skill has been evaluated. In the absence of this information, a group consensus on the probability of outcomes is preferred. Without a large evidence base for calculating probabilities, estimates assessed from a group of plastic surgeons may be acceptable for purposes of breast reconstruction decision analysis.
doi:10.1097/GOX.0000000000000010
PMCID: PMC4044723  PMID: 24910814
4.  Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies 
Neuro-Oncology  2013;15(5):515-534.
Differentiating treatment-induced necrosis from tumor recurrence is a central challenge in neuro-oncology. These 2 very different outcomes after brain tumor treatment often appear similarly on routine follow-up imaging studies. They may even manifest with similar clinical symptoms, further confounding an already difficult process for physicians attempting to characterize a new contrast-enhancing lesion appearing on a patient's follow-up imaging. Distinguishing treatment necrosis from tumor recurrence is crucial for diagnosis and treatment planning, and therefore, much effort has been put forth to develop noninvasive methods to differentiate between these disparate outcomes. In this article, we review the latest developments and key findings from research studies exploring the efficacy of structural and functional imaging modalities for differentiating treatment necrosis from tumor recurrence. We discuss the possibility of computational approaches to investigate the usefulness of fine-grained imaging characteristics that are difficult to observe through visual inspection of images. We also propose a flexible treatment-planning algorithm that incorporates advanced functional imaging techniques when indicated by the patient's routine follow-up images and clinical condition.
doi:10.1093/neuonc/nos307
PMCID: PMC3635510  PMID: 23325863
functional imaging, neuro-oncology; pseudoprogression; tumor recurrence; treatment necrosis
5.  Assessing Women’s Preferences and Preference Modeling for Breast Reconstruction Decision Making 
Background:
Women considering breast reconstruction must make challenging trade-offs among issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction.
Methods:
In this study, we used multiattribute utility theory to combine multiple objectives to yield a summary value using 9 different preference models. We elicited the preferences of 36 women, aged 32 or older with no history of breast cancer, for the patient-reported outcome measures of breast satisfaction, psychosocial well-being, chest well-being, abdominal well-being, and sexual well-being as measured by the BREAST-Q in addition to time lost to reconstruction and out-of-pocket cost. Participants ranked hypothetical breast reconstruction outcomes. We examined each multiattribute utility preference model and assessed how often each model agreed with participants’ rankings.
Results:
The median amount of time required to assess preferences was 34 minutes. Agreement among the 9 preference models with the participants ranged from 75.9% to 78.9%. None of the preference models performed significantly worse than the best-performing risk-averse multiplicative model. We hypothesize an average theoretical agreement of 94.6% for this model if participant error is included. There was a statistically significant positive correlation with more unequal distribution of weight given to the 7 attributes.
Conclusions:
We recommend the risk-averse multiplicative model for modeling the preferences of patients considering different forms of breast reconstruction because it agreed most often with the participants in this study.
doi:10.1097/GOX.0000000000000062
PMCID: PMC4120963  PMID: 25105083
6.  Plastic Surgeon Expertise in Predicting Breast Reconstruction Outcomes for Patient Decision Analysis 
Background:
Decision analysis offers a framework that may help breast cancer patients make good breast reconstruction decisions. A requirement for this type of analysis is information about the possibility of outcomes occurring in the form of probabilities. The purpose of this study was to determine if plastic surgeons are good sources of probability information, both individually and as a group, when data are limited.
Methods:
Seven plastic surgeons were provided with pertinent medical information and preoperative photographs of patients and were asked to assign probabilities to predict number of revisions, complications, and final aesthetic outcome using a questionnaire designed for the study. Logarithmic strictly proper scoring was used to evaluate the surgeons’ abilities to predict breast reconstruction outcomes. Surgeons’ responses were analyzed for calibration and confidence in their answers.
Results:
As individuals, there was variation in surgeons’ ability to predict outcomes. For each prediction category, a different surgeon was more accurate. As a group, surgeons possessed knowledge of future events despite not being well calibrated in their probability assessments. Prediction accuracy for the group was up to 6-fold greater than that of the best individual.
Conclusions:
The use of individual plastic surgeon–elicited probability information is not encouraged unless the individual’s prediction skill has been evaluated. In the absence of this information, a group consensus on the probability of outcomes is preferred. Without a large evidence base for calculating probabilities, estimates assessed from a group of plastic surgeons may be acceptable for purposes of breast reconstruction decision analysis.
doi:10.1097/GOX.0000000000000010
PMCID: PMC4044723  PMID: 24910814
7.  3D Surface Imaging of the Human Female Torso in Upright to Supine Positions 
Medical engineering & physics  2015;37(4):375-383.
Three-dimensional (3D) surface imaging of breasts is usually done with the patient in an upright position, which does not permit comparison of changes in breast morphology with changes in position of the torso. In theory, these limitations may be eliminated if the 3D camera system could remain fixed relative to the woman’s torso as she is tilted from 0 to 90 degrees. We mounted a 3dMDtorso imaging system onto a bariatric tilt table to image breasts at different tilt angles. The images were validated using a rigid plastic mannequin and the metrics compared to breast metrics obtained from 5 subjects with diverse morphology. The differences between distances between the same fiducial marks differed between the supine and upright positions by less than one percent for the mannequin, whereas the differences for distances between the same fiducial marks on the breasts of the 5 subjects differed significantly and could be correlated with body mass index and brassiere cup size for each position change. We show that a tilt table - 3D imaging system can be used to determine quantitative changes in the morphology of ptotic breasts when the subject is tilted to various angles.
doi:10.1016/j.medengphy.2015.01.011
PMCID: PMC4380553  PMID: 25703742
3-dimensional imaging; tilt table; multi-angle imaging; orientation-specific 3D imaging; upright surface scan; angular surface scan; supine surface scan
8.  Automated calculation of symmetry measure on clinical photographs 
1 ABSTRACT
Breast cancer is one of the most prevalent forms of cancer in the world. More than 250,000 American women are diagnosed with breast cancer annually. Fortunately, the survival rate is relatively high and continually increasing due to improved detection techniques and treatment methods. The quality of life of breast cancer survivors is ameliorated by minimizing adverse effects on their physical appearance. Breast reconstruction is important for restoring the survivor’s appearance. In breast reconstructive surgery, there is a need to develop technologies for quantifying surgical outcomes and understanding women’s perceptions of changes in their appearance. Methods for objectively measuring breast anatomy are needed in order to help breast cancer survivors, radiation oncologists, and surgeons quantify changes in appearance that occur with different breast reconstructive surgical options. In this study, we present an automated method for computing a variant of the normalized Breast Retraction Assessment (pBRA), a common measure of symmetry, from routine clinical photographs taken to document breast cancer treatment procedures.
doi:10.1111/j.1365-2753.2010.01477.x
PMCID: PMC2958233  PMID: 20630015
BRA; pBRA; Automated Detection; Digital Photographs; Umbilicus; Nipple Complex; Breast Cancer
9.  Quantifying the Aesthetic Outcomes of Breast Cancer Treatment: Assessment of Surgical Scars from Clinical Photographs 
Accurate assessment of the degree of scaring that results from surgical intervention for breast cancer would enable more effective pre-operative counseling. The resultant scar that accompanies an open surgical intervention may be characterized by variance in thickness, color, and contour. These factors significantly impact the overall appearance of the breast. A number of studies have addressed the mechanical and pathologic aspects of scarring. The majority of these investigations have focused on the physiologic process of scar formation and means to improve the qualities of a scar. Few studies have focused on quantifying the visual impact of scars. This manuscript critically reviews current methods used to assess scars in terms of overall satisfaction after surgery. We introduce objective, quantitative measures for assessing linear breast surgical scars using digital photography. These new measurements of breast surgical scars are based on calculations of contrast and area. We demonstrate, using the intra-class correlation coefficient (ICC), that the new measures are robust to observer variability in annotating the scar region on clinical photographs. As an example of the utility of the new measures, we use them to quantify the aesthetic differences of reconstruction following skin-sparing mastectomy vs. conventional mastectomy.
doi:10.1111/j.1365-2753.2010.01476.x
PMCID: PMC2958242  PMID: 20630016
Aesthetics; Breast Neoplasm; Esthetics; Mastectomy; Outcomes; Prostheses and Implants; Reconstructive Surgical Procedures; Surgical Flaps; Surgical Scars; Treatment Outcome; Quality of Life; Breast Conservation Therapy
10.  In-vivo quantification of human breast deformation associated with the position change from supine to upright 
Medical engineering & physics  2014;37(1):13-22.
Stereophotographic imaging and digital image correlation are used to determine the variation of breast skin deformation as the subject orientation is altered from supine to upright. A change in subject’s position from supine to upright can result in significant stretches in some parts of the breast skin. The maximum of the major principal stretch ratio of the skin is different in different subjects and varies in the range of 1.25–1.60. It is also found that the boundaries of the breast move significantly relative to the skeletal structure and other fixed points such as the sternal notch. Such measurements are crucial since they provide basic data for validation of biomechanical breast models based on finite element formulations.
doi:10.1016/j.medengphy.2014.09.016
PMCID: PMC4297751  PMID: 25456398
Biomechanics; Image correlation; Deformation measurement
11.  Assessment of Breast Aesthetics 
Plastic and reconstructive surgery  2008;121(4):186e-194e.
A good aesthetic outcome is an important endpoint of breast cancer treatment. Subjective ratings, direct physical measurements, measurements on photographs, and assessment by three-dimensional imaging are reviewed and future directions in aesthetic outcome measurements are discussed. Qualitative, subjective scales have frequently been used to assess aesthetic outcomes following breast cancer treatment. However, none of these scales has achieved widespread use because they are typically vague and have low intra- and inter- observer agreement. Anthropometry is not routinely performed because it is impractical to conduct the large studies needed to validate anthropometric measures, i.e., studies in which several observers measure the same subjects multiple times. Quantitative measures based on digital/digitized photographs have yielded acceptable results but have some limitations. Three-dimensional imaging has the potential to enable consistent, objective assessment of breast appearance, including properties, such as volume, that are not available from two-dimensional images. However, further work is needed to define 3D measures of aesthetic properties and how they should be interpreted.
doi:10.1097/01.prs.0000304593.74672.b8
PMCID: PMC3097998  PMID: 18349598
Aesthetics; Breast Neoplasm; Esthetics; Mastectomy; Outcomes; Prostheses and Implants; Reconstructive Surgical Procedures; Surgical Flaps; Treatment Outcome; Quality of Life; Breast Conservation Therapy
12.  Toward Quantifying the Aesthetic Outcomes of Breast Cancer Treatment: Comparison of Clinical Photography and Colorimetry 
Rationale, aims and objectives
Scarring is a significant cause of dissatisfaction for women who undergo breast surgery. Scar tissue may be clinically distinguished from normal skin by aberrant color, rough surface texture, increased thickness (hypertrophy), and firmness. Colorimeters or spectrophotometers can be used to quantitatively assess scar color, but they require direct patient interaction and can cost thousands of dollars By comparison, digital photography is already in widespread use to document clinical outcomes and requires less patient interaction. Thus, assessment of scar coloration by digital photography is an attractive alternative. The goal of this study was to compare color measurements obtained by digital photography and colorimetry.
Method
Agreement between photographic and colorimetric measurements of color were evaluated. Experimental conditions were controlled by performing measurements on artificial scars created by a makeup artist. The colorimetric measurements of the artificial scars were compared to those reported in the literature for real scars in order to confirm the validity of this approach. We assessed the agreement between the colorimetric and photographic measurements of color using a hypothesis test for equivalence, the intra-class correlation coefficient (ICC), and the Bland-Altman method.
Results
Overall, good agreement was obtained for three parameters (L*a*b*) measured by colorimetry and photography from the results of three statistical analyses.
Conclusion
Color measurements obtained by digital photography were equivalent to those obtained using colorimetry. Thus, digital photography is a reliable, cost-effective measurement method of skin color and should be further investigated for quantitative analysis of surgical outcomes.
doi:10.1111/j.1365-2753.2008.00945.x
PMCID: PMC3072466  PMID: 19239578
Aesthetics; Breast Neoplasm; Clinical Photography; Reconstructive Surgical Procedures; Surgical Scars; Treatment Outcome
13.  New scoring methodology improves the sensitivity of the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) in clinical trials 
Introduction
As currently used, the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) has low sensitivity for measuring Alzheimer’s disease progression in clinical trials. A major reason behind the low sensitivity is its sub-optimal scoring methodology, which can be improved to obtain better sensitivity.
Methods
Using item response theory, we developed a new scoring methodology (ADAS-CogIRT) for the ADAS-Cog, which addresses several major limitations of the current scoring methodology. The sensitivity of the ADAS-CogIRT methodology was evaluated using clinical trial simulations as well as a negative clinical trial, which had shown an evidence of a treatment effect.
Results
The ADAS-Cog was found to measure impairment in three cognitive domains of memory, language, and praxis. The ADAS-CogIRT methodology required significantly fewer patients and shorter trial durations as compared to the current scoring methodology when both were evaluated in simulated clinical trials. When validated on data from a real clinical trial, the ADAS-CogIRT methodology had higher sensitivity than the current scoring methodology in detecting the treatment effect.
Conclusions
The proposed scoring methodology significantly improves the sensitivity of the ADAS-Cog in measuring progression of cognitive impairment in clinical trials focused in the mild-to-moderate Alzheimer’s disease stage. This provides a boost to the efficiency of clinical trials requiring fewer patients and shorter durations for investigating disease-modifying treatments.
Electronic supplementary material
The online version of this article (doi:10.1186/s13195-015-0151-0) contains supplementary material, which is available to authorized users.
doi:10.1186/s13195-015-0151-0
PMCID: PMC4642693  PMID: 26560146
14.  A Research Agenda for Appearance Changes Due to Breast Cancer Treatment 
Breast cancer is one of the most prevalent forms of cancer in the US. It is estimated that more than 180,000 American women will be diagnosed with invasive breast cancer in 2008. Fortunately, the survival rate is relatively high and continually increasing due to improved detection techniques and treatment methods. However, maintaining quality of life is a factor often under emphasized for breast cancer survivors. Breast cancer treatments are invasive and can lead to deformation of the breast. Breast reconstruction is important for restoring the survivor’s appearance. However, more work is needed to develop technologies for quantifying surgical outcomes and understanding women’s perceptions of changes in their appearance. A method for objectively measuring breast anatomy is needed in order to help both the breast cancer survivors and their surgeons take expected changes to the survivor’s appearance into account when considering various treatment options. In the future, augmented reality tools could help surgeons reconstruct a survivor’s breasts to match her preferences as much as possible.
PMCID: PMC3085417  PMID: 21655363
breast cancer; 3D imaging of breast; computer-assisted image analysis; quality of life
15.  Computer-Aided Detection of Breast Cancer – Have All Bases Been Covered? 
The use of computer-aided detection (CAD) systems in mammography has been the subject of intense research for many years. These systems have been developed with the aim of helping radiologists to detect signs of breast cancer. However, the effectiveness of CAD systems in practice has sparked recent debate. In this commentary, we argue that computer-aided detection will become an increasingly important tool for radiologists in the early detection of breast cancer, but there are some important issues that need to be given greater focus in designing CAD systems if they are to reach their full potential.
PMCID: PMC3085409  PMID: 21655364
computer-aided detection; breast cancer; mammography; radiology
16.  Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry 
Cancer Informatics  2007;3:219-230.
Noise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a stochastic signal processing approach to analyzing noise from electrical noise sources (i.e., noise from instrumentation) in MALDI TOF mass spectrometry. Noise from instrumentation was hypothesized to be a mixture of thermal noise, 1/f noise, and electric or magnetic interference in the instrument. Parametric power spectral density estimation was conducted to derive the power distribution of noise from instrumentation with respect to frequencies. As expected, the experimental results show that noise from instrumentation contains 1/f noise and prominent periodic components in addition to thermal noise. These periodic components imply that the mass spectrometers used in this study may not be completely shielded from the internal or external electrical noise sources. However, according to a simulation study of human plasma mass spectra, noise from instrumentation does not seem to affect mass spectra significantly. In conclusion, analysis of noise from instrumentation using stochastic signal processing here provides an intuitive perspective on how to quantify noise in mass spectrometry through spectral modeling.
PMCID: PMC2675828  PMID: 19455245
Mass; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Noise; Artifacts; Fourier Analysis; Signal Processing; Computer-Assisted; Computer Simulation; Models; Computer
17.  Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory 
Journal of Medical Imaging  2014;1(3):034001.
Abstract.
Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.
doi:10.1117/1.JMI.1.3.034001
PMCID: PMC4478934  PMID: 26158060
magnetic resonance imaging; tissue segmentation; Bayesian decision theory; level set formulation; Markov random field
18.  Stereoscopic Interpretation of Low-Dose Breast Tomosynthesis Projection Images 
Journal of Digital Imaging  2013;27(2):248-254.
The purpose of this study was to evaluate stereoscopic perception of low-dose breast tomosynthesis projection images. In this Institutional Review Board exempt study, craniocaudal breast tomosynthesis cases (N = 47), consisting of 23 biopsy-proven malignant mass cases and 24 normal cases, were retrospectively reviewed. A stereoscopic pair comprised of two projection images that were ±4° apart from the zero angle projection was displayed on a Planar PL2010M stereoscopic display (Planar Systems, Inc., Beaverton, OR, USA). An experienced breast imager verified the truth for each case stereoscopically. A two-phase blinded observer study was conducted. In the first phase, two experienced breast imagers rated their ability to perceive 3D information using a scale of 1–3 and described the most suspicious lesion using the BI-RADS® descriptors. In the second phase, four experienced breast imagers were asked to make a binary decision on whether they saw a mass for which they would initiate a diagnostic workup or not and also report the location of the mass and provide a confidence score in the range of 0–100. The sensitivity and the specificity of the lesion detection task were evaluated. The results from our study suggest that radiologists who can perceive stereo can reliably interpret breast tomosynthesis projection images using stereoscopic viewing.
doi:10.1007/s10278-013-9648-x
PMCID: PMC3948934  PMID: 24190140
Breast tomosynthesis; Stereoscopic display; 3D perception; Low-dose projections
19.  Eigen-disfigurement model for simulating plausible facial disfigurement after reconstructive surgery 
BMC Medical Imaging  2015;15:12.
Background
Patients with facial cancers can experience disfigurement as they may undergo considerable appearance changes from their illness and its treatment. Individuals with difficulties adjusting to facial cancer are concerned about how others perceive and evaluate their appearance. Therefore, it is important to understand how humans perceive disfigured faces. We describe a new strategy that allows simulation of surgically plausible facial disfigurement on a novel face for elucidating the human perception on facial disfigurement.
Method
Longitudinal 3D facial images of patients (N = 17) with facial disfigurement due to cancer treatment were replicated using a facial mannequin model, by applying Thin-Plate Spline (TPS) warping and linear interpolation on the facial mannequin model in polar coordinates. Principal Component Analysis (PCA) was used to capture longitudinal structural and textural variations found within each patient with facial disfigurement arising from the treatment. We treated such variations as disfigurement. Each disfigurement was smoothly stitched on a healthy face by seeking a Poisson solution to guided interpolation using the gradient of the learned disfigurement as the guidance field vector. The modeling technique was quantitatively evaluated. In addition, panel ratings of experienced medical professionals on the plausibility of simulation were used to evaluate the proposed disfigurement model.
Results
The algorithm reproduced the given face effectively using a facial mannequin model with less than 4.4 mm maximum error for the validation fiducial points that were not used for the processing. Panel ratings of experienced medical professionals on the plausibility of simulation showed that the disfigurement model (especially for peripheral disfigurement) yielded predictions comparable to the real disfigurements.
Conclusions
The modeling technique of this study is able to capture facial disfigurements and its simulation represents plausible outcomes of reconstructive surgery for facial cancers. Thus, our technique can be used to study human perception on facial disfigurement.
Electronic supplementary material
The online version of this article (doi:10.1186/s12880-015-0050-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12880-015-0050-7
PMCID: PMC4396629  PMID: 25885763
Facial disfigurement; Reconstructive surgery; 3D surface image; Simulation; Head and neck cancer
20.  Effect of image registration on longitudinal analysis of retinal nerve fiber layer thickness of non-human primates using Optical Coherence Tomography (OCT) 
Eye and Vision  2015;2:3.
Background
In this paper we determined the benefits of image registration on estimating longitudinal retinal nerve fiber layer thickness (RNFLT) changes.
Methods
RNFLT maps around the optic nerve head (ONH) of healthy primate eyes were measured using Optical Coherence Tomography (OCT) weekly for 30 weeks. One automatic algorithm based on mutual information (MI) and the other semi-automatic algorithm based on log-polar transform cross-correlation using manually segmented blood vessels (LPCC_MSBV), were used to register retinal maps longitudinally. We compared the precision and recall between manually segmented image pairs for the two algorithms using a linear mixed effects model.
Results
We found that the precision calculated between manually segmented image pairs following registration by LPCC_MSBV algorithm is significantly better than the one following registration by MI algorithm (p < <0.0001). Trend of the all-rings and temporal, superior, nasal and inferior (TSNI) quadrants average of RNFLT over time in healthy primate eyes are not affected by registration. RNFLT of clock hours 1, 2, and 10 showed significant change over 30 weeks (p = 0.0058, 0.0054, and 0.0298 for clock hours 1, 2 and 10 respectively) without registration, but stayed constant over time with registration.
Conclusions
The LPCC_MSBV provides better registration of RNFLT maps recorded on different dates than the automatic MI algorithm. Registration of RNFLT maps can improve clinical analysis of glaucoma progression.
Electronic supplementary material
The online version of this article (doi:10.1186/s40662-015-0013-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s40662-015-0013-7
PMCID: PMC4657366  PMID: 26605359
Retinal nerve fiber layer thickness; Glaucoma; Image registration
21.  Retinal nerve fiber layer reflectance for early glaucoma diagnosis 
Journal of glaucoma  2014;23(1):10.1097/IJG.0b013e31829ea2a7.
Purpose
Compare performance of normalized reflectance index (NRI) and retinal nerve fiber layer thickness (RNFLT) parameters determined from OCT images for glaucoma and glaucoma suspect diagnosis.
Methods
Seventy-five eyes from seventy-one human subjects were studied: 33 controls, 24 glaucomatous, and 18 glaucoma-suspects. RNFLT and NRI maps were measured using two custom-built OCT systems and the commercial instrument RTVue. Using area under the receiver operating characteristic (ROC) curve, RNFLT and NRI measured in seven RNFL locations were analyzed to distinguish between control, glaucomatous, and glaucoma-suspect eyes.
Results
The mean NRI of the control group was significantly larger than the means of glaucomatous and glaucoma-suspect groups in most RNFL locations for all three OCT systems (p<0.05 for all comparisons). NRI performs significantly better than RNFLT at distinguishing between glaucoma-suspect and control eyes using RTVue OCT (p=0.008). The performances of NRI and RNFLT for classifying glaucoma-suspect vs. control eyes were statistically indistinguishable for PS-OCT-EIA (p=0.101) and PS-OCT-DEC (p=0.227). The performances of NRI and RNFLT for classifying glaucomatous vs. control eyes were statistically indistinguishable (PS-OCT-EIA: p=0.379; PS-OCT-DEC: p=0.338; RTVue OCT: p=0.877).
Conclusions
NRI is a promising measure for distinguishing between glaucoma-suspect and control eyes and may indicate disease in the pre-perimetric stage. Results of this pilot clinical study warrant a larger study to confirm the diagnostic power of NRI for diagnosing pre-perimetric glaucoma.
doi:10.1097/IJG.0b013e31829ea2a7
PMCID: PMC3844555  PMID: 23835671
glaucoma; optical coherence tomography; retinal nerve fiber layer
22.  Path-length-multiplexed scattering-angle-diverse optical coherence tomography for retinal imaging 
Optics letters  2013;38(21):4374-4377.
A low-resolution path-length-multiplexed scattering angle diverse optical coherence tomography (PM-SAD-OCT) is constructed to investigate the scattering properties of the retinal nerve fiber layer (RNFL). Low-resolution PM-SADOCT retinal images acquired from a healthy human subject show the variation of RNFL scattering properties at retinal locations around the optic nerve head. The results are consistent with known retinal ganglion cell neural anatomy and principles of light scattering. Application of PM-SAD-OCT may provide potentially valuable diagnostic information for clinical retinal imaging.
PMCID: PMC3903005  PMID: 24177097
23.  Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities 
doi:10.1136/amiajnl-2013-002315
PMCID: PMC3822124  PMID: 24114330
Imaging Informatics; Image Analysis; Computer Aided Diagnosis; Precision Medicine; Whole Slide Imaging; Image Annotation
24.  Monte Carlo lookup table-based inverse model for extracting optical properties from tissue-simulating phantoms using diffuse reflectance spectroscopy 
Journal of Biomedical Optics  2013;18(3):037003.
Abstract.
We present a Monte Carlo lookup table (MCLUT)-based inverse model for extracting optical properties from tissue-simulating phantoms. This model is valid for close source-detector separation and highly absorbing tissues. The MCLUT is based entirely on Monte Carlo simulation, which was implemented using a graphics processing unit. We used tissue-simulating phantoms to determine the accuracy of the MCLUT inverse model. Our results show strong agreement between extracted and expected optical properties, with errors rate of 1.74% for extracted reduced scattering values, 0.74% for extracted absorption values, and 2.42% for extracted hemoglobin concentration values.
doi:10.1117/1.JBO.18.3.037003
PMCID: PMC3584151  PMID: 23455965
optical properties; diffuse reflectance spectroscopy; graphical processing unit; lookup table; inverse model
25.  Verification of a two-layer inverse Monte Carlo absorption model using multiple source-detector separation diffuse reflectance spectroscopy 
Biomedical Optics Express  2013;5(1):40-53.
A two-layer Monte Carlo lookup table-based inverse model is validated with two-layered phantoms across physiologically relevant optical property ranges. Reflectance data for source-detector separations of 370 μm and 740 μm were collected from these two-layered phantoms and top layer thickness, reduced scattering coefficient and the top and bottom layer absorption coefficients were extracted using the inverse model and compared to the known values. The results of the phantom verification show that this method is able to accurately extract top layer thickness and scattering when the top layer thickness ranges from 0 to 550 μm. In this range, top layer thicknesses were measured with an average error of 10% and the reduced scattering coefficient was measured with an average error of 15%. The accuracy of top and bottom layer absorption coefficient measurements was found to be highly dependent on top layer thickness, which agrees with physical expectation; however, within appropriate thickness ranges, the error for absorption properties varies from 12–25%.
doi:10.1364/BOE.5.000040
PMCID: PMC3891344  PMID: 24466475
(170.6510) Spectroscopy, tissue diagnostics; (100.3190) Inverse problems

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