To evaluate the clinical acceptability of semiautomated methods for the measurement of mesothelioma tumor thickness in computed tomography (CT) scans.
A computer interface was developed to allow the acquisition of semiautomated mesothelioma tumor thickness measurements, which require the manual selection of a point along the outer margin of the tumor in a CT section. After application of an automated lung segmentation method, the computer automatically identifies a corresponding point along the inner margin of the tumor (as represented by the lung boundary), constructs a line segment between the manually selected outer tumor margin point and the computer-determined inner tumor margin point, and computes tumor thickness as the length of this line segment. Three radiologists and oncologists independently reviewed line segments representing the semiautomated measurements generated by three different algorithms at 134 measurement sites in the CT scans of 22 mesothelioma patients. The observers either accepted a measurement line segment or modified it through the interface. Differences between the initial semiautomated measurements and the measurements as modified by the observers were analyzed.
The frequency with which observers accepted the semiautomated measurements without modification was as high as 86%. Of all measurements across all observers and methods (1,206 measurements), 89% were changed by 2 mm or less.
We have developed semiautomated methods to measure mesothelioma tumor thickness. The potential of these methods has been demonstrated through an observer study. We expect these methods to become important tools for the efficient quantification of tumor extent.
"To date, worldwide only one solution based on a semi-automated method is available , which was applied to measure thickness of mesothelioma tumors. This method requires the manual selection of a point along the outer margin of the tumor in a CT slice, followed by the application of an automated lung segmentation method. "
[Show abstract][Hide abstract] ABSTRACT: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. For its early stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings.
We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura.
Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickenings were provided.
Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantify its performance.
Methods of Information in Medicine 02/2007; 46(3):324-31. DOI:10.1160/ME9050 · 2.25 Impact Factor
"To the best of our knowledge, only one semi-automatic system worldwide was developed by Armato III et al  in order to detect the pleural thickenings. This method requires manual interaction before the automatic detection of the pleural contours of lungs. "
[Show abstract][Hide abstract] ABSTRACT: Aufbauend auf früheren Arbeiten wurde ein Bildverarbeitungssystem entwickelt, welches pleurale Verdickungen automatisch lokalisiert
und visualisiert. Es liefert reproduzierbare, quantitative Daten, die eine genauere Beobachtung der Verdickungen ermöglichen
als die konventionelle Befundungsmethode, und reduziert den für die Befundung nötigen Zeitaufwand. Die automatische Detektion
findet innerhalb eines zweistufigen Algorithmus statt, der zuerst aus allen Schichten des Datensatzes die Pleurakonturen extrahiert
und darauf aufbauend die Verdickungen in den Pleurakonturen detektiert. Da die Änderung der Form einer Verdickung ein wichtiges
Kriterium bei der Entscheidung ist, ob eine Verdickung entartet, wurde eine Möglichkeit zur Visualisierung der Verdickungen
und der Lungenflügel implementiert. Diese können nun aus allen Perspektiven betrachtet werden. Unterschiede in den Verdickungen
zweier aufeinanderfolgender Scans können so erkannt werden.
Bildverarbeitung für die Medizin 2006, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 19. - 21. März 2006 in Hamburg; 01/2006
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