Comparison of Automatic Segmentation and Preprocessing Approaches for Dynamic Total-Body 3D Pet Images with Different Pet Tracers.
Segmentation is a routine step in PET image analysis, and few automatic tools have been developed for it. However, excluding supervised methods with their own limitations, they are typically designed for older, small images and the implementations are no longer publicly available. Here, we test if d...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 1; pp. 382 - 400 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191694210&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191694210 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2026 vid: 39 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191694210 190842556 191694210 191694210 10.1007/s10278-025-01540-4 191694210 ppf: 382 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparison of Automatic Segmentation and Preprocessing Approaches for Dynamic Total-Body 3D Pet Images with Different Pet Tracers. aug: au: Jaakkola, Maria K. Rivera Pineda, Marcela Xiomara Díaz, Rafael Rantala, Maria Jalo, Anna Kärpijoki, Henri Saari, Teemu Maaniitty, Teemu Keller, Thomas Louhi, Heli Wahlroos, Saara Haaparanta-Solin, Merja Solin, Olof Hentilä, Jaakko Helin, Jatta S. Nissinen, Tuuli A. Eskola, Olli Rajander, Johan Knuuti, Juhani Virtanen, Kirsi A. affil: https://ror.org/029pk6x14 Turku PET Centre, University of Turku, Åbo Akademi University, and Turku University Hospital, Turku, Finland sug: subj: Positron-Emission Tomography Methods Automation Imaging, Three-Dimensional Image Processing, Computer Assisted Clustering Algorithms Human Animal Studies Rats Comparative Studies Machine Learning Validation Studies Sensitivity and Specificity Descriptive Statistics Data Analysis Software Noise Brain Radiography Heart Radiography Lung Radiography Pituitary Gland Radiography Thyroid Gland Radiography Kidney Radiography Liver Radiography Aorta Radiography ab: Segmentation is a routine step in PET image analysis, and few automatic tools have been developed for it. However, excluding supervised methods with their own limitations, they are typically designed for older, small images and the implementations are no longer publicly available. Here, we test if different commonly used building blocks of the automatic methods work with large modern total-body PET images. Dynamic total-body images from five different datasets are used for evaluation purposes, and the tested algorithms cover wide range of different preprocessing approaches and unsupervised segmentation methods. The validation is done by comparing the obtained segments to manually drawn ones using Jaccard index, Dice score, precision, and recall as measures of match. Out of the 17 considered segmentation methods, only 6 were computationally usable and provided enough segments for the needs of this study. Among these six feasible methods, hierarchical clustering and HDBSCAN had systematically the lowest Jaccard indices with the manual segmentations, whereas both GMM and k-means had median Jaccards of 0.58 over different organ segments and data sets. GMM outperformed k-means in human data, but with rat images, the two methods had equally good performance k-means having slightly stronger precision and GMM recall. We conclude that most of the commonly used unsupervised segmentation methods are computationally infeasible with the modern PET images, classical clustering algorithms k-means and especially Gaussian mixture model being the most promising candidates for further method development. Even though preprocessing, particularly denoising, improved the results, small organs remained difficult to segment. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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