Tomas Sakinis

774 total citations
10 papers, 499 citations indexed

About

Tomas Sakinis is a scholar working on Radiology, Nuclear Medicine and Imaging, Physiology and Archeology. According to data from OpenAlex, Tomas Sakinis has authored 10 papers receiving a total of 499 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Physiology and 3 papers in Archeology. Recurrent topics in Tomas Sakinis's work include Forensic Anthropology and Bioarchaeology Studies (3 papers), Body Composition Measurement Techniques (3 papers) and Nutrition and Health in Aging (3 papers). Tomas Sakinis is often cited by papers focused on Forensic Anthropology and Bioarchaeology Studies (3 papers), Body Composition Measurement Techniques (3 papers) and Nutrition and Health in Aging (3 papers). Tomas Sakinis collaborates with scholars based in Norway, United States and Puerto Rico. Tomas Sakinis's co-authors include Panagiotis Korfiatis, Petro Kostandy, Kenneth A. Philbrick, Timothy L. Kline, Bradley J. Erickson, Alexander D. Weston, Naoki Takahashi, Motokazu Sugimoto, Jaya Prakash and Naveen Paluru and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiology and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Tomas Sakinis

10 papers receiving 491 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tomas Sakinis Norway	7	265	125	103	92	68	10	499
Christopher P. Bridge United States	14	205 0.8×	140 1.1×	114 1.1×	80 0.9×	50 0.7×	43	593
Petro Kostandy United States	7	218 0.8×	111 0.9×	66 0.6×	106 1.2×	54 0.8×	8	446
Yiming Lei China	12	120 0.5×	73 0.6×	52 0.5×	35 0.4×	47 0.7×	31	428
Hong Kyu Kim South Korea	16	439 1.7×	39 0.3×	73 0.7×	59 0.6×	73 1.1×	44	858
Hans Frimmel Sweden	13	183 0.7×	145 1.2×	68 0.7×	38 0.4×	128 1.9×	31	544
Γεωργία Γεωργίου Greece	12	157 0.6×	93 0.7×	41 0.4×	91 1.0×	61 0.9×	34	445
Tobias Hepp Germany	12	263 1.0×	16 0.1×	89 0.9×	86 0.9×	54 0.8×	26	405
Martin Segeroth Switzerland	5	337 1.3×	27 0.2×	68 0.7×	180 2.0×	78 1.1×	11	516
Soichiro Miki Japan	14	323 1.2×	32 0.3×	152 1.5×	94 1.0×	71 1.0×	51	659
Nick Lasse Beetz Germany	11	111 0.4×	84 0.7×	33 0.3×	32 0.3×	8 0.1×	29	311

Countries citing papers authored by Tomas Sakinis

Since Specialization

Citations

This map shows the geographic impact of Tomas Sakinis'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 Tomas Sakinis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomas Sakinis more than expected).

Fields of papers citing papers by Tomas Sakinis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tomas Sakinis. 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 Tomas Sakinis. The network helps show where Tomas Sakinis may publish in the future.

Co-authorship network of co-authors of Tomas Sakinis

This figure shows the co-authorship network connecting the top 25 collaborators of Tomas Sakinis. A scholar is included among the top collaborators of Tomas Sakinis 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 Tomas Sakinis. Tomas Sakinis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Kvaal, Sigrid I., et al.. (2023). Age prediction in sub-adults based on MRI segmentation of 3rd molar tissue volumes. International Journal of Legal Medicine. 137(3). 753–763. 5 indexed citations

Bleka, Øyvind, et al.. (2023). MRI segmentation of tooth tissue in age prediction of sub-adults — a new method for combining data from the 1st, 2nd, and 3rd molars. International Journal of Legal Medicine. 138(3). 939–949. 1 indexed citations

Kvaal, Sigrid I., et al.. (2023). Prediction of Age Older than 18 Years in Sub-adults by MRI Segmentation of 1st and 2nd Molars. International Journal of Legal Medicine. 137(5). 1515–1526. 2 indexed citations

Avenarius, Derk, Atle Bjørnerud, Berit Flatø, et al.. (2022). Automated segmentation of magnetic resonance bone marrow signal: a feasibility study. Pediatric Radiology. 52(6). 1104–1114. 8 indexed citations

Sakinis, Tomas, et al.. (2022). Body composition assessment by artificial intelligence from routine computed tomography scans in colorectal cancer: Introducing BodySegAI. SHILAP Revista de lepidopterología. 7(3). 55–64. 11 indexed citations

Paluru, Naveen, Aveen Dayal, Tomas Sakinis, et al.. (2021). Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images. IEEE Transactions on Neural Networks and Learning Systems. 32(3). 932–946. 129 indexed citations

Henriksen, Hege Berg, Ane Sørlie Kværner, Tomas Sakinis, et al.. (2021). Quantification of adipose tissues by Dual-Energy X-Ray Absorptiometry and Computed Tomography in colorectal cancer patients. Clinical Nutrition ESPEN. 43. 360–368. 11 indexed citations

Server, Andrés, et al.. (2020). Understanding Pediatric Neuroimmune Disorder Conflicts: A Neuroradiologic Approach in the Molecular Era. Radiographics. 40(5). 1395–1411. 6 indexed citations

Philbrick, Kenneth A., Alexander D. Weston, Zeynettin Akkus, et al.. (2019). RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. Journal of Digital Imaging. 32(4). 571–581. 78 indexed citations

10.

Weston, Alexander D., Panagiotis Korfiatis, Timothy L. Kline, et al.. (2018). Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Radiology. 290(3). 669–679. 248 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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