Oona Rainio

1.2k total citations · 1 hit paper
40 papers, 548 citations indexed

About

Oona Rainio is a scholar working on Radiology, Nuclear Medicine and Imaging, Geometry and Topology and Applied Mathematics. According to data from OpenAlex, Oona Rainio has authored 40 papers receiving a total of 548 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Geometry and Topology and 11 papers in Applied Mathematics. Recurrent topics in Oona Rainio's work include Analytic and geometric function theory (12 papers), Medical Imaging Techniques and Applications (10 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Oona Rainio is often cited by papers focused on Analytic and geometric function theory (12 papers), Medical Imaging Techniques and Applications (10 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Oona Rainio collaborates with scholars based in Finland, Qatar and United States. Oona Rainio's co-authors include Riku Klén, Jarmo Teuho, Матти Вуоринен, Thorsten Rudroff, Mohamed M. S. Nasser, Juhani Knuuti, Simona Malaspina, Jukka Kemppainen, Chunlei Han and Vesa Oikonen and has published in prestigious journals such as Scientific Reports, European Journal of Nuclear Medicine and Molecular Imaging and Applied Sciences.

In The Last Decade

Oona Rainio

30 papers receiving 534 citations

Hit Papers

Evaluation metrics and statistical tests for machine lear... 2024 2026 2025 2024 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Oona Rainio Finland 8 110 86 42 40 40 40 548
Adam Mahdi United Kingdom 15 62 0.6× 73 0.8× 98 2.3× 47 1.2× 123 3.1× 53 824
Ana‐Maria Staicu United States 19 265 2.4× 101 1.2× 21 0.5× 93 2.3× 20 0.5× 64 1.2k
Linglong Kong Canada 16 118 1.1× 82 1.0× 6 0.1× 49 1.2× 9 0.2× 68 623
Valeria Vitelli Norway 16 253 2.3× 40 0.5× 13 0.3× 65 1.6× 19 0.5× 46 901
Riku Klén Finland 16 118 1.1× 230 2.7× 98 2.3× 71 1.8× 222 5.5× 95 1.1k
Pierre Lafaye de Micheaux France 16 83 0.8× 57 0.7× 24 0.6× 104 2.6× 7 0.2× 45 702
Florentina Bunea United States 13 136 1.2× 38 0.4× 63 1.5× 85 2.1× 5 0.1× 50 645
Leszek Wojnar Poland 11 66 0.6× 53 0.6× 65 1.5× 77 1.9× 3 0.1× 39 785
Jochen Einbeck United Kingdom 11 74 0.7× 31 0.4× 9 0.2× 35 0.9× 8 0.2× 57 461
Connor J. Dalzell Canada 4 125 1.1× 23 0.3× 19 0.5× 33 0.8× 14 0.3× 5 629

Countries citing papers authored by Oona Rainio

Since Specialization
Citations

This map shows the geographic impact of Oona Rainio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Oona Rainio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oona Rainio more than expected).

Fields of papers citing papers by Oona Rainio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Oona Rainio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Oona Rainio. The network helps show where Oona Rainio may publish in the future.

Co-authorship network of co-authors of Oona Rainio

This figure shows the co-authorship network connecting the top 25 collaborators of Oona Rainio. A scholar is included among the top collaborators of Oona Rainio based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Oona Rainio. Oona Rainio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rainio, Oona, et al.. (2025). Pulmonary blood flow quantification in humans from 15O-water PET. Annals of Nuclear Medicine. 39(6). 600–607. 1 indexed citations
2.
Rainio, Oona & Riku Klén. (2025). Modified Dice Coefficients for Evaluation of Tumor Segmentation from PET Images: A Proof-of-Concept Study. Journal of Imaging Informatics in Medicine. 39(1). 785–793.
3.
Rainio, Oona. (2025). Inequalities for a new hyperbolic type metric. 11(2).
4.
Rainio, Oona, et al.. (2025). Methods for estimating full width at half maximum. Signal Image and Video Processing. 19(4). 1 indexed citations
5.
Rudroff, Thorsten, Oona Rainio, & Riku Klén. (2024). Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective. Brain Sciences. 14(8). 831–831. 5 indexed citations
6.
Rudroff, Thorsten, Riku Klén, Oona Rainio, & Jetro J. Tuulari. (2024). The Untapped Potential of Dimension Reduction in Neuroimaging: Artificial Intelligence-Driven Multimodal Analysis of Long COVID Fatigue. Brain Sciences. 14(12). 1209–1209. 3 indexed citations
7.
Rainio, Oona, et al.. (2024). One-click annotation to improve segmentation by a convolutional neural network for PET images of head and neck cancer patients. Network Modeling Analysis in Health Informatics and Bioinformatics. 13(1). 1 indexed citations
8.
Rainio, Oona, et al.. (2024). Comparison of thresholds for a convolutional neural network classifying medical images. International Journal of Data Science and Analytics. 20(3). 2093–2099. 3 indexed citations
9.
Rainio, Oona, Toshiyuki Sugawa, & Матти Вуоринен. (2024). Uniformly Perfect Sets, Hausdorff Dimension, and Conformal Capacity. Journal of Geometric Analysis. 34(6). 1 indexed citations
10.
Rainio, Oona, et al.. (2024). Exploring Generative Adversarial Network-Based Augmentation of Magnetic Resonance Brain Tumor Images. Applied Sciences. 14(24). 11822–11822.
11.
Rudroff, Thorsten, Oona Rainio, & Riku Klén. (2024). Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma. Brain Sciences. 14(11). 1111–1111. 1 indexed citations
12.
Rainio, Oona, et al.. (2023). Automatic Segmentation of Head and Neck Cancer from PET-MRI Data Using Deep Learning. Journal of Medical and Biological Engineering. 43(5). 532–540. 10 indexed citations
13.
Rainio, Oona, Otto Ettala, Marko Seppänen, et al.. (2023). New method of using a convolutional neural network for 2D intraprostatic tumor segmentation from PET images. Research on Biomedical Engineering. 39(4). 905–913. 6 indexed citations
14.
Rainio, Oona, et al.. (2023). Classification of head and neck cancer from PET images using convolutional neural networks. Scientific Reports. 13(1). 10528–10528. 14 indexed citations
15.
Rainio, Oona, Chunlei Han, Jarmo Teuho, et al.. (2023). Carimas: An Extensive Medical Imaging Data Processing Tool for Research. Journal of Digital Imaging. 36(4). 1885–1893. 26 indexed citations
16.
Rainio, Oona. (2023). Inequalities for the generalized point pair function. Lithuanian Mathematical Journal. 63(3). 396–410. 1 indexed citations
17.
Nasser, Mohamed M. S., Oona Rainio, Antti Rasila, et al.. (2022). Polycircular domains, numerical conformal mappings, and moduli of quadrilaterals. Advances in Computational Mathematics. 48(5). 7 indexed citations
18.
Rainio, Oona & Матти Вуоринен. (2022). Introducing a New Intrinsic Metric. 7 indexed citations
19.
Rainio, Oona. (2022). Intrinsic metrics in ring domains. 1 indexed citations
20.
Nasser, Mohamed M. S., Oona Rainio, & Матти Вуоринен. (2021). Condenser capacity and hyperbolic diameter. Qatar University QSpace (Qatar University). 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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