Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering

Author:

Jaakkola Maria K.12ORCID,Rantala Maria1,Jalo Anna34ORCID,Saari Teemu12ORCID,Hentilä Jaakko1ORCID,Helin Jatta S.34ORCID,Nissinen Tuuli A.34ORCID,Eskola Olli5ORCID,Rajander Johan6ORCID,Virtanen Kirsi A.12,Hannukainen Jarna C.1ORCID,López-Picón Francisco134ORCID,Klén Riku12ORCID

Affiliation:

1. Turku PET Centre, University of Turku, Turku, Finland

2. Turku PET Centre, Turku University Hospital, Turku, Finland

3. MediCity Research Laboratory, University of Turku, Turku, Finland

4. PET Preclinical Laboratory, Turku PET Centre, University of Turku, Turku, Finland

5. Radiopharmaceutical Chemistry Laboratory, Turku PET Centre, University of Turku, Turku, Finland

6. Accelerator Laboratory, Turku PET Centre, Abo Akademi University, Turku, Finland

Abstract

Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan level is much less studied due to the available total-body data being limited to animal studies. Now, the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ-level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First, we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. These criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k -means and Gaussian mixture model (GMM), for further analyses. We combined k -means with two different preprocessing approaches, namely, principal component analysis (PCA) and independent component analysis (ICA). Then, we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F] fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k -means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k -means combined with PCA the most promising candidate for further development. However, even with the best methods, the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images.

Funder

Sigrid Juséliuksen Säätiö

Publisher

Hindawi Limited

Subject

Radiology, Nuclear Medicine and imaging

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