A brief peripheral motion contrast threshold test predicts older drivers' hazardous behaviors in simulated driving.

Our research group has previously demonstrated that the peripheral motion contrast threshold (PMCT) test predicts older drivers' self-report accident risk, as well as simulated driving performance. However, the PMCT is too lengthy to be a part of a battery of tests to assess fitness to drive. Therefore, we have developed a new version of this test, which takes under two minutes to administer. We assessed the motion contrast thresholds of 24 younger drivers (19-32) and 25 older drivers (65-83) with both the PMCT-10min and the PMCT-2min test and investigated if thresholds were associated with measures of simulated driving performance. Younger participants had significantly lower motion contrast thresholds than older participants and there were no significant correlations between younger participants' thresholds and any measures of driving performance. The PMCT-10min and the PMCT-2min thresholds of older drivers' predicted simulated crash risk, as well as the minimum distance of approach to all hazards. This suggests that our tests of motion processing can help predict the risk of collision or near collision in older drivers. Thresholds were also correlated with the total lane deviation time, suggesting a deficiency in processing of peripheral flow and delayed detection of adjacent cars. The PMCT-2min is an improved version of a previously validated test, and it has the potential to help assess older drivers' fitness to drive.

Self-assessed performance improves statistical fusion of image labels.

PURPOSE: Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone to inter-individual differences. While fully automated methods have successfully targeted many anatomies, automated methods have not yet been developed for numerous essential structures (e.g., the internal structure of the spinal cord as seen on magnetic resonance imaging). Collaborative labeling is a new paradigm that offers a robust alternative that may realize both the throughput of automation and the guidance of experts. Yet, distributing manual labeling expertise across individuals and sites introduces potential human factors concerns (e.g., training, software usability) and statistical considerations (e.g., fusion of information, assessment of confidence, bias) that must be further explored. During the labeling process, it is simple to ask raters to self-assess the confidence of their labels, but this is rarely done and has not been previously quantitatively studied. Herein, the authors explore the utility of self-assessment in relation to automated assessment of rater performance in the context of statistical fusion. METHODS: The authors conducted a study of 66 volumes manually labeled by 75 minimally trained human raters recruited from the university undergraduate population. Raters were given 15 min of training during which they were shown examples of correct segmentation, and the online segmentation tool was demonstrated. The volumes were labeled 2D slice-wise, and the slices were unordered. A self-assessed quality metric was produced by raters for each slice by marking a confidence bar superimposed on the slice. Volumes produced by both voting and statistical fusion algorithms were compared against a set of expert segmentations of the same volumes. RESULTS: Labels for 8825 distinct slices were obtained. Simple majority voting resulted in statistically poorer performance than voting weighted by self-assessed performance. Statistical fusion resulted in statistically indistinguishable performance from self-assessed weighted voting. The authors developed a new theoretical basis for using self-assessed performance in the framework of statistical fusion and demonstrated that the combined sources of information (both statistical assessment and self-assessment) yielded statistically significant improvement over the methods considered separately. CONCLUSIONS: The authors present the first systematic characterization of self-assessed performance in manual labeling. The authors demonstrate that self-assessment and statistical fusion yield similar, but complementary, benefits for label fusion. Finally, the authors present a new theoretical basis for combining self-assessments with statistical label fusion.

Texture analysis improves level set segmentation of the anterior abdominal wall.

PURPOSE: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. METHODS: In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors' approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall. RESULTS: Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2-5 mm) for methods that did not use the texture. CONCLUSIONS: The authors' approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.

Imaging analysis of hepatoblastoma resectability across neoadjuvant chemotherapy.

PURPOSE: Hepatoblastomas often require neoadjuvant chemotherapy to facilitate partial hepatectomy, which necessitates freedom of tumor borders from the confluence of hepatic veins (COHV), portal vein bifurcation (PVB), and retrohepatic inferior vena cava (IVC). This study aimed to clarify the effect of incremental neoadjuvant cycles on the AHEP0731 protocol criteria of hepatoblastoma resectability. METHODS: Hepatoblastoma responses to neoadjuvant chemotherapy were analyzed among patients (n=23) treated at two children's hospitals between 1996 and 2010. Using digital imaging data, ellipsoid and point-based models were created to measure tumor volume regression and respective distances from tumor borders nearest to the COHV, PVB, and IVC. RESULTS: Hepatoblastoma volumes regressed with incremental neoadjuvant chemotherapy cycles (p<0.001). Although tumor borders regressed away from the COHV (p=0.008), on average only 1.1mm was gained. No change from tumor borders to the PVB was detected (p=0.102). Distances from tumor borders to the IVC remained stable at one hospital (p=0.612), but increased only 0.15 mm every 10 days of therapy at the other (p=0.002). Neoadjuvant chemotherapy induced slightly more tumors to meet the threshold vascular margin of 1cm (baseline to completion): COHV, 11 (47.8%) to 17 (73.9%; p=0.058); PVB, 11 (47.8%) to 15 (65.2%; p=0.157); and IVC, 4 (17.4%) to 10 (43.5%; p=0.034). No differences were detected in demographic or disease-specific characteristics between patients who did or did not achieve this 1-cm margin after conclusion of chemotherapy. CONCLUSION: Hepatoblastoma volumes regress significantly with increasing neoadjuvant chemotherapy cycles. However, tumors often remain anchored to the major hepatic vasculature, showing marginal improvement in resectability criteria.

Quantitative Anatomical Labeling of the Anterior Abdominal Wall.

Ventral hernias (VHs) are abnormal openings in the anterior abdominal wall that are common side effects of surgical intervention. Repair of VHs is the most commonly performed procedure by general surgeons worldwide, but VH repair outcomes are not particularly encouraging (with recurrence rates up to 43%). A variety of open and laparoscopic techniques are available for hernia repair, and the specific technique used is ultimately driven by surgeon preference and experience. Despite routine acquisition of computed tomography (CT) for VH patients, little quantitative information is available on which to guide selection of a particular approach and/or optimize patient-specific treatment. From anecdotal interviews, the success of VH repair procedures correlates with hernia size, location, and involvement of secondary structures. Herein, we propose an image labeling protocol to segment the anterior abdominal area to provide a geometric basis with which to derive biomarkers and evaluate treatment efficacy. Based on routine clinical CT data, we are able to identify inner and outer surfaces of the abdominal walls and the herniated volume. This is the first formal presentation of a protocol to quantify these structures on abdominal CT. The intra- and inter rater reproducibilities of this protocol are evaluated on 4 patients with suspected VH (3 patients were ultimately diagnosed with VH while 1 was not). Mean surfaces distances of less than 2mm were achieved for all structures.

Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study.

The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24-43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments; notably, quantitative metrics based on image-processing are not used. We propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. To date, automated segmentation algorithms have not been presented to quantify the abdominal wall and potential hernias. In this pilot study with four clinically acquired CT scans on post-operative patients, we demonstrate a novel approach to geometric classification of the abdominal wall and essential abdominal features (including bony landmarks and skin surfaces). Our approach uses a hierarchical design in which the abdominal wall is isolated in the context of the skin and bony structures using level set methods. All segmentation results were quantitatively validated with surface errors based on manually labeled ground truth. Mean surface errors for the outer surface of the abdominal wall was less than 2mm. This approach establishes a baseline for characterizing the abdominal wall for improving VH care.

Use of spinal cord stimulation in the treatment of phantom limb pain: case series and review of the literature.

INTRODUCTION: Despite technical advances in spinal cord stimulation (SCS), there is a paucity of recent literature regarding SCS for phantom limb pain. METHODS: Between January 2003 and May 2008, four patients at M.D. Anderson Cancer Center underwent SCS for intractable phantom limb pain. A retrospective chart review was performed to assess outcomes and complications. A PubMed search was performed to review previously published series regarding the efficacy of SCS for phantom limb pain. RESULTS: Postoperatively, all patients subjectively reported excellent pain relief (>80%). Patients were all followed with the Brief Pain Inventory. Patients 1 to 3 each reported at wo-point decrease in their usual amount of pain using the numerical rating scale. Patient 4's numerical pain scale was unchanged. When using an 11-point scale to assess other symptomology along 10 dimensions, patients 1 to 3 demonstrated a decrease in their total symptom score by 13, 14, and 4 points, respectively. Patient 4 reported an increase by 5 points in his total symptom score. With regard to complications, patient 2 developed an allergic dermatitis to the generator requiring revision with a polyfluoroethylene (GorTex) pouch. Patient 3 developed a surgical site infection after an implantable pulse generator change. Patients 2 to 4 were very satisfied with their stimulator and would choose to undergo implantation again, with patient 1 having an equivocal response. CONCLUSIONS: For selected patients who have not obtained adequate relief with medical management, SCS for phantom limb pain can prove an effective intervention.