Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering
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 providi...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2023/3819587 |
| _version_ | 1797393067099029504 |
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| author | Maria K. Jaakkola Maria Rantala Anna Jalo Teemu Saari Jaakko Hentilä Jatta S. Helin Tuuli A. Nissinen Olli Eskola Johan Rajander Kirsi A. Virtanen Jarna C. Hannukainen Francisco López-Picón Riku Klén |
| author_facet | Maria K. Jaakkola Maria Rantala Anna Jalo Teemu Saari Jaakko Hentilä Jatta S. Helin Tuuli A. Nissinen Olli Eskola Johan Rajander Kirsi A. Virtanen Jarna C. Hannukainen Francisco López-Picón Riku Klén |
| author_sort | Maria K. Jaakkola |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T23:57:45Z |
| format | Article |
| id | doaj.art-6aa8f6b2dabe4568b37638f9a31ae1fe |
| institution | Directory Open Access Journal |
| issn | 1687-4196 |
| language | English |
| last_indexed | 2024-03-08T23:57:45Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj.art-6aa8f6b2dabe4568b37638f9a31ae1fe2023-12-13T00:00:30ZengHindawi LimitedInternational Journal of Biomedical Imaging1687-41962023-01-01202310.1155/2023/3819587Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised ClusteringMaria K. Jaakkola0Maria Rantala1Anna Jalo2Teemu Saari3Jaakko Hentilä4Jatta S. Helin5Tuuli A. Nissinen6Olli Eskola7Johan Rajander8Kirsi A. Virtanen9Jarna C. Hannukainen10Francisco López-Picón11Riku Klén12Turku PET CentreTurku PET CentreMediCity Research LaboratoryTurku PET CentreTurku PET CentreMediCity Research LaboratoryMediCity Research LaboratoryRadiopharmaceutical Chemistry LaboratoryAccelerator LaboratoryTurku PET CentreTurku PET CentreTurku PET CentreTurku PET CentreClustering 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.http://dx.doi.org/10.1155/2023/3819587 |
| spellingShingle | Maria K. Jaakkola Maria Rantala Anna Jalo Teemu Saari Jaakko Hentilä Jatta S. Helin Tuuli A. Nissinen Olli Eskola Johan Rajander Kirsi A. Virtanen Jarna C. Hannukainen Francisco López-Picón Riku Klén Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering International Journal of Biomedical Imaging |
| title | Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering |
| title_full | Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering |
| title_fullStr | Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering |
| title_full_unstemmed | Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering |
| title_short | Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering |
| title_sort | segmentation of dynamic total body 18f fdg pet images using unsupervised clustering |
| url | http://dx.doi.org/10.1155/2023/3819587 |
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