Computer aided lung nodule detection on high resolution CT data Rafael Wiemker a , Patrik Rogallab, Andre Zwartkruis c , Thomas Bla erta a Philips Research Laboratories Hamburg, Germany b Dept. of Radiology, Charit e Hospital, Humboldt University Berlin, Germany c Philips Medical Systems Best, The Netherlands
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 speci cally designed detection algorithm which is applied to 1 mm slice data from multi-array 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 Houns eld values due to the absence of the partial volume e ect (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 mm and reconstruction intervals between 0.5 and 1 mm, with hard- and soft-tissue reconstruction lters. 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.
Keywords: Computer aided detection, CAD, lung nodules, segmentation, shape analysis, high resolution CT, HRCT, lung cancer screening
1. INTRODUCTION The detection and diagnosis of pulmonary nodules in CT data sets of the thorax is a standard procedure in radiological practise. Pulmonary nodules are often benign, but they may also be an indication for lung cancer, or may be metastases from other cancer types. In any case, early detection of lung nodules is crucial, either for close observation or biopsy to di erentiate between benign or malignant nodules, or for timely therapy. There are clear indications that early detection of lung cancer can improve the survival rate. Moreover, it is hoped that lung cancer screening of high risk patient groups may signi cantly increase the rate of lung cancer cases which are diagnosed before the cancer has metastasized. Lung nodules can be detected particularly well by CT, since they show good contrast in the lung parenchyma and | in contrast to projection X-ray | cannot be hidden by ribs etc. However, although in principle detectable in CT, a non negligible fraction of small nodules may be overlooked by the radiologist, particularly if they are located centrally and hidden in a maze of vessels of similar size. Therefore, computer assistance for detecting lung nodules in CT data sets has been suggested and investigated as early as 1989. The underlying idea is not that the diagnosis is delegated to a machine, but rather that a machine algorithm acts as a support to the radiologist and points out locations of suspicious objects, so that the overall sensitivity (detection rate) is raised. This could be important particularly in screening situations, with a massive reviewing load of CT studies. The overwhelming majority of computer aided lung nodule detection approaches has been designed for and tested on conventional 5{10 mm CT slice thickness. However, the reported sensitivity and speci city rates were often 1{3
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low and have so far failed to reach the level of clinical acceptance and usefulness. On the one hand, the detection rate was often disappointing, and on the other hand the false positive rate sometimes so high that the annoyance and tiring-out of the radiologist might possibly cancel out any potential gain in sensitivity. E.g. the 10 mm slice study of Fiebich et al. (2001) reports 38% sensitivity (on 68 nodules) with 6 false positives per patient. Lee et al. (2001) report 72% sensitivity (on 98 nodules) with 31 false positives per patient. Moreover, both studies have restricted themselves to nodules with size 5 mm. The principal problem of computerized nodule detection in thick slices of 5{10 mm is that the opacity of nodules which are smaller than the slice thickness is thinned out by the partial volume e ect. Therefore it is impossible to set a certain Houns eld value as a threshold for potential nodules. Rather, many di erent thresholds have to be tried, and false positives cannot be rejected simply due to a low Houns eld value. Another approach to cope with the unkown Houns eld level caused by the partial volume e ect is to apply a morphological tophat- or Quoit- lter which measured the di erence in attenuation between the center and surrounding of a possible nodule. This is quite costly as the lters have to applied in each possible size (diameter). Another problem of thick CT slices is that a reasoning mechanism is required to recognize constellations where a small nodule is overshadowed by a vessel of similar size within the 10 mm projection. Also thin vessels may appear disconnected when running obliquely through the slice images and thus be mistaken for nodules. High resolution CT data with slice thickness 1 mm allows the detection of very small nodules. High resolution CT (HRCT) has so far been used mainly for characterization and classi cation of already singled-out individual nodules. Only recently HRCT data has been used for the nodule detection within a complete thorax data set. Fan et al. (2001) evaluated 112 nodule candidates 2 mm, but no sensitivity (detection rate) was speci ed, and pleural nodules were not considered. The obvious advantage of HRCT data is that with voxel dimensions of e.g. 0.70.70.7 mm the partial volume e ect can be neglected for nodules of size > 1 mm. The Houns eld values can be evaluated as absolute attenuation values, and the fully isotropic three dimensional shape information of potential nodules can be utilized to resolve ambiguities between pulmonary nodules and vessels. The utilization of high resolution CT (HRCT) for computer aided nodule detection holds the promise of increasing sensitivity and speci city in such a way as to achieve a break-through for CAD in clinical practise. 7
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2. HIGH RESOLUTION CT IMAGE DATA The image data evaluated in this paper was recorded in the years 2000{2001 during clinical routine in the radiology department of the Charite university hospital, Berlin, by multi slice CT (Toshiba Aquillion, 4 slices per half-second rotation). Each thorax data set comprises 300{500 slice images with 512 512 pixels. The x-y -resolution (in-slice) varies between 0.5{1.0 mm, and also the z -resolution (reconstruction interval) varies between 0.5{1.0 mm, with a slice thickness of 1 mm (overlapping slices). The scans were recorded over the entire thorax at 120 kV and 100 mAs. More than 20 000 CT slices from 50 thoracic scans were analyzed by our nodule detection program, and 12 scans have so far been reviewed by a radiologist.
3.1. Lung segmentation
3. NODULE DETECTION ALGORITHM
Prior to the nodule detection, some approaches at rst segment the lung out of the overall thorax data set in order to reduce the amount of image data to be analyzed (Fig. 1, left-hand). However, this removes as well nodules which are attached to the lung wall (pleural nodules, see Fig. 4, right-hand). Other approaches include these lung wall attached nodules by a morphological opening of the lung volume, or by correcting the lung circumference by enforcing convexity at locations of rapidly changing curvature. In our experience this may induce many false positive candidates which have to rejected in a later stage. Moreover, the morphologically included objects have a completely arti cial surface at the cut-o boundary (the convex envelope of the lung volume) which may complicate the subsequent spatial shape analysis of the potential nodule candidates. Therefore we have chosen an approach where in a binary mask image the airspace around the patient body is lled (thereby also eliminating the tray and objects on the outside of the body) but the voxels outside of the lung are not removed (Fig. 1, right-hand). Rather, the full extent of each slice image is analyzed in the search of seed points for possible nodule candidates. In each binary mask image slice, a 2D lter is applied which identi es all structures similar to circles or half-circles. 20
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Figure 1. Left: Lung structures segmented out of the overall thorax data (coronal MIP).
Right: In our approach, the whole slice image is scanned for circular or semi-circular structures (after thresholding and lling of the surrounding airspace) in order to include also nodules attached to the lung wall (see pleural nodule in the right lung).
3.2. Isotropic resolution and the missing partial volume e ect In earlier approaches to lung nodule detection on thick slices (5{10 mm) the nodule candidates are often identi ed on thresholded binary images by virtue of the criterion that at a certain HU-threshold the nodule is a connected component (blob) which is con ned to a small volume. In other words, a region growing (or connected component labeling) would eventually come to a stop after the nodule is lled, in contrast to a vessel (tubular structure) in which the growth would continue throughout the vessel tree. The nodules may be connected to vessels as well, of course (see Fig. 4). The underlying idea of multiple thresholding, or the Quoit- lter is that the thin vessels connecting to the nodule show lower Houns eld values than the nodule itself, due to the partial volume e ect. So there would always be a di erence in Houns eld value which could be measured (Quoit- lter) or exploited by an appropriate HU-threshold to separate the nodule from the connecting vessels. For one thing, the use of multiple thresholding with high resolution CT (HRCT) would be very time-consuming since we have to deal with the 10-fold amount of image data. More important, however, is that the whole detection paradigm does not work on thin CT slices. For the case of nodule and connecting vessels thicker than 1 mm in diameter, the missing partial volume e ect means that we often encounter the situation that there is no possible HUthreshold which would actually cut o the connecting vessels from the nodule. Hence, nodule detection algorithms on HRCT data need to concentrate on shape analysis rather than on nding appropriate thresholds. The principal criterion for nodules in HRCT is whether all vessels connecting to a nodule candidate are signi cantly smaller in diameter than the nodule itself. This can be determined e.g. by a region growing scheme. The shape analysis of our algorithm evaluates compactness, thickness of connecting vessels, and average Houns eld value and HU distribution within the nodule candidate. The average Houns eld value is particularly useful for rejction of false positive candidates. The 3-dimensional shape analysis can be carried out on binary threshold images. Due to the absence of the partial volume e ect the HU-threshold can be a global one for the whole CT data set. In our analysis of nodule candidates from 36 of the CT studies we have found that the average Houns eld value is almost independent of the nodule size (Fig. 2). The average Houns eld values of all nodule candidates are clearly above {400 HU. The bulk of 8,23
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all nodule candidates has an average Houns eld values between {200 and {100 HU (Fig. 3, left-hand.). We have also measured the optimal HU-threshold in local volumes of interest around 129 nodule candidates. The optimality is de ned in such a way as to maximize the mean gradient at the HU-isosurface between nodule and the surrounding parenchyma. The results (Fig. 3, right-hand) suggest that the strongest contrast between nodule and background is reached around {300 HU. The eÆciency of the lung nodule detection algorithm is based on the fact that in HRCT a single Houns eld threshold suÆces to identify all seed points for near circular or semi-circular nodule candidates. 24
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Mean Houns eld value [HU] of nodule candidates vs. nodule diameter [mm] of 941 nodule candidates from 36 CT studies. The mean Houns eld value is only weakly correlated with the nodule size, and all nodules are clearly above {400 HU. Right: Nodule candidates in CT data reconstructed with a soft tissue reconstruction lter (157 nodule candidates from 15 CT studies). 30 140 25 120
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Figure 3. Left: Histogram of the mean Houns eld value of 941 nodule candidates (see data of Fig. 2, left-hand). Right: Histogram of the optimal HU-threshold value of 129 local volumes of interest around nodule candidates.
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Figure 4. Left: Example of a small nodule (surface rendering at threshold {500 HU; with arrow pointing at nodule,
and dummy gure to indicate viewing direction). Right: Example of a nodule attached to the lung wall with connecting vessels (volume rendering of a local volume of interest of 505050 voxels).
Figure 5. Evaluation of the nodules by the radiologist:
Left: Axial slice with two nodules (arrow-marked nodule is of 2 mm size). Right: Overview image (coronal MIP), with currently displayed slice (left) indicated as white line. The overview image can be chosen between various projections: MIP, DRR, blended MIP / DRR, or segmented lung volume. Mouse click on marked nodule candidates sets the currently displayed axial view to the appropriate slice image, and vice versa. The nodule candidates are also accessible through a list which can be ordered according to slice location, diameter, likelihood, and average Houns eld value.
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Figure 6. Examples of pulmonary nodules
(out of a single thorax study; ordered in ascending size, the smallest 3 mm, top left).
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Figure 7. Examples of nodules attached to the pleural surface (out of a single thorax study, ordered in ascending size).
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Figure 8. Maximum intensity projections (MIPs) of a local volume of interest of 505050 voxels centered around
the nodules. The nodules are ordered in descending size, with the largest (top left) consisting of 3 500 voxels down to the smallest consisting of only 50 voxels (ca. 3 mm).
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4. RESULTS 4.1. Evaluation of detection rate and false positive rate The nodule detection algorithm is a fully automatic oine-program which starts out from the input of the high resolution CT data set (as described in section 2) and does not require any interaction. The program runs typically 3{5 minutes for 300{500 slices per thorax scan on a SUN Sparc Ultra processor (300 MHz) and produces a list of nodule candidates. A variety of detected nodules are depicted in Fig. 6{8. The nodule candidates are presented to the radiologist in a graphical user interface (see Fig. 5). Each nodule candidate is annotated with a likelihood, size, HU average, and geometrical descriptors. The nodule candidates are marked in the axial slice images. They are also accessible by mouse click into an coronal overview image or by a candidate list which can be ordered according to slice location, diameter, likelihood, and average Houns eld value. An experienced radiologist then reviewed each thorax data set and classi ed the computer marked nodule candidates as true or false positives. Moreover, the radiologist marked nodules which have been missed entirely by the detection algorithm (false negatives). From 12 CT studies (patients) which have been reviewed as of yet, we observe the following results for sensitivity and false positive rate (a speci city cannot be computed as there is no meaningful de nition of true negative candidates) : size
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The graph shows the dependence of the detection rate from the minimum nodule size (i.e. the sensitivity for the set of all nodules with size d mm is plotted as a function of d). We observe that the sensitivity is ca. 95% and quite independent of the nodule size once they are larger or equal to 2 mm, and that there are no missed nodules at all larger than 8 mm. The false positive candidates are essentially all smaller than 5 mm, and thus the false positive rate drops below 1 per study for nodules 4 mm.
4.2. Limiting detection size We observe that a reliable detection and shape analysis becomes possible if a minimum number of ca. 30 voxels is available for analysis. On high resolution CT data with a voxel resolution of 0.70.70.7 mm , say, a nodule of 2 mm diameter corresponds approximately to a 333 block of voxels (for comparison see a nodule with 50 voxels in Fig. 8, bottom right). 3
4.3. Lung wall attached nodules More than half of the computation time of the lung nodule detection program is spent on the search for nodules attached to the lung wall. The detection of hemi-spherical objects is conceptually as well as numerically more demanding since there a more degrees of freedom than for isolated nodules which are completely surrounded by the lung parenchyma. Di erent possible orientations of the lung wall have to tested (see examples in Fig. 7). Our results indicate that the detection rate for pleural nodules is equally high as for the parenchyma surrounded nodules.
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4.4. Principal cases of false positive candidates We have observed two principal cases of false positive candidates: I Vessels which are running close to the heart and seemingly disrupted by caridac motion, and thus appear as a series of small isolated nodules (Fig. 9).
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Hilus vessels which branch out of the mediastinum and decrease rapidly in diameter so that they are mistaken as lung wall attached nodules with connecting vessels (Fig. 10). Other false positive candidates are caused by thick vessel bifurcations and bronchial wall thickenings.
Figure 9. Left: False positive candidates from cardiac motion artifacts in the left lung. Right: Enlarged surface rendering (with arrow indicating the false positive candidates).
Figure 10. One true positive nodule (cross hair), and one false positive candidate (below), both close to the trachea. Left: Sagittal cut-plane; the upper nodule is true, whereas the lower candidate is a strongly curved vessel. Right: Volume rendering of the true positive nodule seen along the trachea close to the carina. 686
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5. CONCLUSIONS We are presenting results from computer aided lung nodule detection on high resolution CT (HRCT) image data. Most previous approaches to computer aided detection of pulmonary nodules have been designed for and applied to conventional CT image data with slice thickness of 5{10 mm. Then the sensitivity and speci city of automatic nodule detection is compromised by the partial volume e ect for small nodules. Nodule detection algorithms for HRCT data must be designed in a di erent way: They must be eÆcient since they have to cope with the ca. 10-fold amount of image data, and they cannot cut o small vessels from nodules by means of their di erence in Houns eld values, due to the missing partial volume e ect. On the other hand, they have the crucial advantage that the absolute Houns eld values can be directly used for recognition of nodules and rejection of false positive candidates. Moreover, the ne isotropic resolution allows a fully three dimensional shape analysis for nodules larger than 2 mm. We have processed more than 20 000 slices from 50 thoracic high resolution CT scans with 1 mm slice thickness and on average 0.7 mm reconstruction interval, recorded during clinical routine at the radiology department of the Charite university hospital, Berlin. Each data set consists of 300{500 slices. 12 data sets have so far been reviewed by an experienced radiologist. Isolated nodules surrounded by the lung parenchyma as well as nodules attached to the lung wall (pleural nodules) have been taken into account. Out of 203 nodules with diameter 2 mm (including pleural nodules), the detection algorithm found 193 (sensitivity of 95%), with 4.4 false positives per patient. We observe that with HRCT reliable automatic detection seems feasible for nodules larger or equal to 2 mm in diameter. The false positive rate drops below 1 per study for nodules 4 mm. The use of high resolution CT may raise the levels of sensitivity and speci city signi cantly, and thus bring the break-through for acceptance of CAD in clinical routine, particularly with respect to cancer screening.
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