Biomedical subjects

Adam Starkey

Publications and source records attributed to Adam Starkey.

5 recordsLinked to original sources

PubMed2006

Evaluation of lung MDCT nodule annotation across radiologists and methods.

RATIONALE AND OBJECTIVES: Integral to the mission of the National Institutes of Health-sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary. MATERIALS AND METHODS: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists' spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects. RESULTS: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively. CONCLUSION: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume.

Artificial Intelligence↗

PubMed2006

Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections.

Segmentation of the lungs within magnetic resonance (MR) scans is a necessary step in the computer-based analysis of thoracic MR images. This process is often confounded by image acquisition artifacts and disease-induced morphological deformation. We have developed an automated method for lung segmentation that is insensitive to these complications. The automated method was applied to 23 thoracic MR scans (413 sections) obtained from 10 patients. Two radiologists manually outlined the lung regions in a random sample of 101 sections (n=202 lungs), and the extent to which disease or artifact confounded lung border visualization was evaluated. Accuracy of lung regions extracted by the automated segmentation method was quantified by comparison with the radiologist-defined lung regions using an area overlap measure (AOM) that ranged from 0 (disjoint lung regions) to 1 (complete overlap). The AOM between each observer and the automated method was 0.82 when averaged over all lungs. The average AOM in the lung bases, where lung segmentation is most difficult, was 0.73.

Aged↗

PubMed2006

Variability in mesothelioma tumor response classification.

OBJECTIVE: The objective of our study was to evaluate observer variability in the measurement of temporal change in mesothelioma tumor thickness and in the resulting tumor response classification from CT scans. In addition, the performance of a semiautomated measurement method was evaluated. MATERIALS AND METHODS: Four observers individually used an interface that displayed two serial CT scans from the same patient to measure mesothelioma tumor thickness on the follow-up CT scans of 22 patients based on baseline scan measurements. During one session, observers acquired measurements on the follow-up scans based on written reports of baseline scan measurements; in another session, baseline scan measurements were superimposed on the baseline scan for direct visual comparison. Follow-up scan measurements also were obtained from a semiautomated method. Measurement variability and tumor response classification concordance were evaluated for manual measurements acquired in both modes and for semiautomated measurements. RESULTS: Although only a small increase in tumor response classification concordance rate was obtained with the visual approach (84.8%) relative to the more standard written-report approach (82.6%), the actual measurements acquired by observers were statistically significantly different between the two approaches (p = 0.03). Both the semiautomated measurements and the resulting tumor response classifications were consistent with manual measurements. CONCLUSION: The presentation of baseline scan tumor measurements affects measurements acquired on follow-up scans and could influence tumor response classification. The potential utility of semiautomated tumor thickness measurements was shown in the context of measuring tumor response.

Adult↗

PubMed2004

Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques.

Our purpose in this study was to evaluate the variability of manual mesothelioma tumor thickness measurements in computed tomography (CT) scans and to assess the relative performance of six computerized measurement algorithms. The CT scans of 22 patients with malignant pleural mesothelioma were collected. In each scan, an initial observer identified up to three sites in each of three CT sections at which tumor thickness measurements were to be made. At each site, five observers manually measured tumor thickness through a computer interface. Three observers repeated these measurements during three separate sessions. Inter- and intra-observer variability in the manual measurement of tumor thickness was assessed. Six automated measurement algorithms were developed based on the geometric relationship between a specified measurement site and the automatically extracted lung regions. Computer-generated measurements were compared with manual measurements. The tumor thickness measurements of different observers were highly correlated (r > or = 0.99); however, the 95% limits of agreement for relative inter-observer difference spanned a range of 30%. Tumor thickness measurements generated by the computer algorithms also correlated highly with the average of observer measurements (r > or = 0.93). We have developed computerized techniques for the measurement of mesothelioma tumor thickness in CT scans. These techniques achieved varying levels of agreement with measurements made by human observers.

Adult↗

PubMed2004

Automated matching of temporally sequential CT sections.

In the evaluation of patient response to therapy through measurements on thoracic computed tomography (CT) scans, the selection of anatomically equivalent sections in temporally sequential scans is required. We developed an automated method based on normalized mutual information (NMI) to expedite the selection of anatomically equivalent sections. The method requires as input two temporally sequential CT scans from the same patient. A specified section from the baseline scan is then compared with the sections of a follow-up scan. Each section in the follow-up scan is successively translated and rotated relative to the baseline section, and NMI is calculated. The section in the follow-up scan that yields the highest NMI with respect to the baseline section is selected as the matching section. The method was applied to a database of 22 pairs of temporally sequential CT scans from mesothelioma patients. Five observers manually selected their choice of the best anatomically matched section for each of three predetermined sections in the 22 baseline scans, and the range of selected sections was recorded. The automated method was applied to the same baseline sections to determine the computer-based anatomically matched sections in the corresponding follow-up scan. The automated process was performed using both original CT sections and sections automatically segmented so that only intrathoracic pixels contributed to NMI calculations. The accuracy of the automated method was quantified on a section-by-section basis by comparison with the range of sections selected by the observers. The automated method without segmentation selected equivalent sections within the observers' range for 54 of the 66 matching tasks (81.8%). An 11% improvement was achieved when thoracic segmentation was performed as a pre-processing step.

Adult↗