Computer-aided lung nodule detection on high-resolution CT data

Rafael Wiemker; Patrick Rogalla; Andre Zwartkruis; Thomas Blaffert

doi:10.1117/12.467210

9 May 2002 Computer-aided lung nodule detection on high-resolution CT data

Rafael Wiemker, Patrick Rogalla, Andre Zwartkruis, Thomas Blaffert

Author Affiliations +

Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002) https://doi.org/10.1117/12.467210
Event: Medical Imaging 2002, 2002, San Diego, California, United States

Abstract

Most of the previous approaches to computer aided lung nodule detection have been designed for and tested on conventional CT with slice thickness of 5-10 mm. In this paper, we report results of a specifically designed detection algorithm which is applied to 1 mm slice data from multi slice CT. We see two prinicipal advantages of high resolution CT data with respect to computer aided lung nodule detection: First of all, the algorithm can evaluate the fully isotropic three dimensional shape information of potential nodules and thus resolve ambiguities between pulmonary nodules and vessels. Secondly, the use of 1 mm slices allows the direct utilization of the Hounsfield values due to the absence of the partial volume effect (for objects larger than 1 mm). Computer aided detection of small lung nodules (>= 2 mm) may thus experience a break-through in clinical relevance with the use of high resolution CT. The detection algorithm has been applied to image data sets from patients in clinical routine with a slice thickness of 1\ts mm and reconstruction intervals between 0.5 and 1 mm, with hard- and soft-tissue reconstruction filters. Each thorax data set comprises 300-500 images. More than 20 000 CT slices from 50 CT studies were analyzed by the computer program, and 12 studies have so far been reviewed by an experienced radiologist. Of 203 nodules with diameter >= 2 mm (including pleura-attached nodules), the detection algorithm found 193 (sensitivity of 95%), with 4.4 false positives per patient. Nodules attached to the lung wall are algorithmically harder to detect, but we observe the same high detection rate. The false positive rate drops below 1 per study for nodules >= 4 mm.

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Rafael Wiemker, Patrick Rogalla, Andre Zwartkruis, and Thomas Blaffert "Computer-aided lung nodule detection on high-resolution CT data", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); https://doi.org/10.1117/12.467210

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