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. 2014 Mar;61(3):711-24.
doi: 10.1109/TBME.2013.2288258. Epub 2013 Nov 5.

Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models

Brent Foster, Ulas Bagci, Ziyue Xu, Bappaditya Dey, Brian Luna, William Bishai, Sanjay Jain, Daniel J Mollura

Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models

Brent Foster et al. IEEE Trans Biomed Eng. 2014 Mar.

Abstract

Pulmonary infections often cause spatially diffuse and multi-focal radiotracer uptake in positron emission tomography (PET) images, which makes accurate quantification of the disease extent challenging. Image segmentation plays a vital role in quantifying uptake due to the distributed nature of immuno-pathology and associated metabolic activities in pulmonary infection, specifically tuberculosis (TB). For this task, thresholding-based segmentation methods may be better suited over other methods; however, performance of the thresholding-based methods depend on the selection of thresholding parameters, which are often suboptimal. Several optimal thresholding techniques have been proposed in the literature, but there is currently no consensus on how to determine the optimal threshold for precise identification of spatially diffuse and multi-focal radiotracer uptake. In this study, we propose a method to select optimal thresholding levels by utilizing a novel intensity affinity metric within the affinity propagation clustering framework. We tested the proposed method against 70 longitudinal PET images of rabbits infected with TB. The overall dice similarity coefficient between the segmentation from the proposed method and two expert segmentations was found to be 91.25 ±8.01% with a sensitivity of 88.80 ±12.59% and a specificity of 96.01 ±9.20%. High accuracy and heightened efficiency of our proposed method, as compared to other PET image segmentation methods, were reported with various quantification metrics.

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Figures

Fig. 1
Fig. 1
(Color online.) Example of a TB infected rabbit lung that shows diffuse and multi-focal areas of radiotracer uptake. (a) axial, (b) sagittal, and (c) coronal slice of the rabbit are shown, and a lung volume rendering is provided in (d).
Fig. 2
Fig. 2
(Color online.) Here is an overview of the proposed PET segmentation framework. For (a) a given PET image and (b) its histogram, (c) its pdf is estimated by KDE via diffusion. (d) The smoothed pdf is used to derive novel similarity parameters (e) which are then clustered using affinity propagation (f). Resulting segmentations are shown in (g) 2-D and (h) 3-D. The colorbar in (h) shows the SUVmax level of the lesions.
Fig. 3
Fig. 3
(Color online.) Proposed calculation for the affinity between points i and j on the histogram. Objects O1, O2, and O3 represent the classifications that can be made from the gray level histogram.
Fig. 4
Fig. 4
(Left) Original histogram from PET image of a masked rabbit lung. (Middle) Histogram after KDE via diffusion and piecewise cubic interpolation. (Right) Final histogram after exponential smoothing with window size = 20. The approximate shape of the original histogram is preserved.
Fig. 5
Fig. 5
(Color online.) (a) and (b) Examples of exemplar-based AP clustering results on 100 2-D randomly generated points. (c) and (d) are AP applied to 40 randomly generated 3-D points and their groupings. For both examples, the center point of each group is the groups exemplar.
Fig. 6
Fig. 6
TB lesions were sorted by area found from expert segmentation and divided into 3 groups small (0–3.45 cm2), medium (3.45–6.84 cm2), and large (6.84–30.67 cm2) with equivalent number of lesions per group. A large variation of sizes was used to remove bias from the segmentation results.
Fig. 7
Fig. 7
Linear regression graph of the segmentation area from our proposed method versus observer 1, observer 2, and the average threshold segmentation between observer 1 and 2. The segmentations provided by observer 1 and observer 2 are plotted to demonstrate the large inter-observer variation.
Fig. 8
Fig. 8
Bland–Altman plot of results from the PET rabbit lung images. The solid line represents the mean difference between the two segmentations while the dashed lines is the 95% confidence interval.
Fig. 9
Fig. 9
(Color online.) Segmentation results of PET images from the rabbit model. (a) and (d) Original PET images. (b) and (e) original image is overlaid with the segmentation boundaries found from the proposed method. (c) and (f) the same segmentation result is also provided in a different visualization with colored group labels.
Fig. 10
Fig. 10
Quantitative results of proposed method versus several sate-of-the-art methods against the average expert thresholding value. Supervised k-Means is k-Means using manually defined number of clusters per image.
Fig. 11
Fig. 11
(Color online.) Qualitative comparison of several state-of-the-art PET image segmentation methods. For the proposed method, AP Thresholding, only the boundary of the highest uptake group is shown for easier comparison.
Fig. 12
Fig. 12
Analysis of the robustness of the threshold value selection was performed. The images were segemented using percent changes of the thresholding values found using the proposed method and DSC value is calculated.
Fig. 13
Fig. 13
(Color online.) Testing images were segmented using various parameterization for n and m in the proposed affinity function. The DSC results as compared to the expert defined ground truth are provided.
Fig. 14
Fig. 14
(Color online.) The strength of the affinities using the proposed affinity function applied to three selected example intensities (specified by red dots on the original image in the top row) is given using red to signify a stronger affinity while blue represents a weaker strength (bottom row). The final segmentation is given in the bottom right.
Fig. 15
Fig. 15
(Color online.) AP clustering algorithm allows the identification of the exemplars at any iteration. A qualitative view on the convergence of the proposed method is provided.
Fig. 16
Fig. 16
DSC rates between the proposed segmentation framework utilizing different affinity functions as compared to the ground truth. Geodesic is utilizing only the geodesic distance as a similiarity function between the data point. Euclidean refers to the Euclidean distance while exponential refers to the Gaussian distance function.
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