Žiga Špiclin

1.2k total citations
53 papers, 714 citations indexed

About

Žiga Špiclin is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Žiga Špiclin has authored 53 papers receiving a total of 714 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 19 papers in Radiology, Nuclear Medicine and Imaging and 17 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Žiga Špiclin's work include Medical Image Segmentation Techniques (18 papers), Cerebrovascular and Carotid Artery Diseases (16 papers) and Intracranial Aneurysms: Treatment and Complications (13 papers). Žiga Špiclin is often cited by papers focused on Medical Image Segmentation Techniques (18 papers), Cerebrovascular and Carotid Artery Diseases (16 papers) and Intracranial Aneurysms: Treatment and Complications (13 papers). Žiga Špiclin collaborates with scholars based in Slovenia, United States and South Korea. Žiga Špiclin's co-authors include Franjo Pernuš, Boštjan Likar, Tim Jerman, Alfiia Galimzianova, F. Pernuš, Miran Bürmen, Jaka Katrašnik, Saša Šega Jazbec, Alojz Ihan and Vojko Strojnik and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and NeuroImage.

In The Last Decade

Žiga Špiclin

49 papers receiving 700 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Žiga Špiclin Slovenia 14 298 259 134 127 88 53 714
Seyed‐Ahmad Ahmadi Germany 18 339 1.1× 304 1.2× 190 1.4× 67 0.5× 210 2.4× 48 1.2k
Jin Tang China 17 434 1.5× 428 1.7× 203 1.5× 54 0.4× 133 1.5× 71 1.1k
Fucang Jia China 23 547 1.8× 529 2.0× 291 2.2× 129 1.0× 74 0.8× 85 1.6k
Clayton Chi‐Chang Chen Taiwan 20 85 0.3× 167 0.6× 94 0.7× 212 1.7× 177 2.0× 68 1.1k
Roland Opfer Germany 14 94 0.3× 258 1.0× 65 0.5× 99 0.8× 60 0.7× 54 759
Oskar Maier Germany 8 220 0.7× 168 0.6× 85 0.6× 68 0.5× 121 1.4× 12 624
Denis P. Shamonin Netherlands 10 207 0.7× 441 1.7× 170 1.3× 146 1.1× 20 0.2× 22 842
Zhuangzhi Yan China 16 133 0.4× 301 1.2× 143 1.1× 61 0.5× 38 0.4× 87 749
László G. Nyúl Hungary 13 440 1.5× 461 1.8× 85 0.6× 32 0.3× 34 0.4× 45 853

Countries citing papers authored by Žiga Špiclin

Since Specialization
Citations

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

Fields of papers citing papers by Žiga Špiclin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Žiga Špiclin

This figure shows the co-authorship network connecting the top 25 collaborators of Žiga Špiclin. A scholar is included among the top collaborators of Žiga Špiclin 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 Žiga Špiclin. Žiga Špiclin 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.
Park, Jung Cheol, et al.. (2025). Prediction of intracranial aneurysm rupture from computed tomography angiography using an automated artificial intelligence framework. Computers in Biology and Medicine. 197(Pt A). 110965–110965.
2.
Špiclin, Žiga, et al.. (2024). Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures. Biomedicines. 12(9). 2139–2139. 2 indexed citations
3.
Špiclin, Žiga, et al.. (2023). Predicting future multiple sclerosis disease progression from MR scans. 4. 103–103. 1 indexed citations
4.
Špiclin, Žiga, et al.. (2023). BASE: Brain Age Standardized Evaluation. NeuroImage. 285. 120469–120469. 13 indexed citations
5.
Špiclin, Žiga, et al.. (2023). A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography. Biomedicines. 11(11). 2921–2921. 13 indexed citations
6.
Rep, Sebastijan, Marko Hočevar, Tomaž Kocjan, et al.. (2022). Detection and localization of hyperfunctioning parathyroid glands on [ 18 F]fluorocholine PET/ CT using deep learning – model performance and comparison to human experts. Radiology and Oncology. 56(4). 440–452. 2 indexed citations
7.
Strojnik, Vojko, et al.. (2021). Impact of aerobic exercise on clinical and magnetic resonance imaging biomarkers in persons with multiple sclerosis: An exploratory randomized controlled trial. Journal of Rehabilitation Medicine. 53(4). jrm00178–jrm00178. 21 indexed citations
8.
Likar, Boštjan, et al.. (2017). 3D–2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms. International Journal of Computer Assisted Radiology and Surgery. 13(2). 193–202. 11 indexed citations
9.
Galimzianova, Alfiia, et al.. (2017). A Novel Public MR Image Dataset of Multiple Sclerosis Patients With Lesion Segmentations Based on Multi-rater Consensus. Neuroinformatics. 16(1). 51–63. 73 indexed citations
10.
Špiclin, Žiga, et al.. (2017). Monoplane 3D–2D registration of cerebral angiograms based on multi-objective stratified optimization. Physics in Medicine and Biology. 62(24). 9377–9394. 1 indexed citations
11.
Pernuš, Franjo, et al.. (2016). Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database. Neuroinformatics. 14(4). 403–420. 22 indexed citations
12.
Pernuš, Franjo, et al.. (2016). A framework for automatic creation of gold-standard rigid 3D–2D registration datasets. International Journal of Computer Assisted Radiology and Surgery. 12(2). 263–275. 12 indexed citations
13.
Jerman, Tim, Franjo Pernuš, Boštjan Likar, & Žiga Špiclin. (2016). Enhancement of Vascular Structures in3D and 2D Angiographic Images. IEEE Transactions on Medical Imaging. 35(9). 2107–2118. 187 indexed citations
14.
Galimzianova, Alfiia, Franjo Pernuš, Boštjan Likar, & Žiga Špiclin. (2015). Stratified mixture modeling for segmentation of white-matter lesions in brain MR images. NeuroImage. 124(Pt A). 1031–1043. 16 indexed citations
15.
Pernuš, Franjo, et al.. (2015). Simultaneous 3D–2D image registration and C‐arm calibration: Application to endovascular image‐guided interventions. Medical Physics. 42(11). 6433–6447. 17 indexed citations
16.
Špiclin, Žiga, et al.. (2013). 3D-2D Registration of Cerebral Angiograms: A Method and Evaluation on Clinical Images. IEEE Transactions on Medical Imaging. 32(8). 1550–1563. 42 indexed citations
17.
Špiclin, Žiga, et al.. (2012). Groupwise Registration of Multimodal Images by an Efficient Joint Entropy Minimization Scheme. IEEE Transactions on Image Processing. 21(5). 2546–2558. 21 indexed citations
18.
Špiclin, Žiga, Simon K. Warfield, Boštjan Likar, & Franjo Pernuš. (2008). Registration of MRI and EEG based on internal and external anatomical similarities. PubMed Central. 11. 762. 4 indexed citations
19.
Špiclin, Žiga, et al.. (2008). EEG to MRI Registration Based on Global and Local Similarities of MRI Intensity Distributions. Lecture notes in computer science. 11(Pt 1). 762–770. 6 indexed citations
20.
Špiclin, Žiga, et al.. (2007). Geometrical and Statistical Visual Inspection of Imprinted Tablets. Machine Vision and Applications. 412–415. 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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