Gerard Sanromà

3.2k total citations
28 papers, 510 citations indexed

About

Gerard Sanromà is a scholar working on Computer Vision and Pattern Recognition, Pediatrics, Perinatology and Child Health and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Gerard Sanromà has authored 28 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 10 papers in Pediatrics, Perinatology and Child Health and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Gerard Sanromà's work include Fetal and Pediatric Neurological Disorders (10 papers), Medical Image Segmentation Techniques (8 papers) and Neonatal and fetal brain pathology (7 papers). Gerard Sanromà is often cited by papers focused on Fetal and Pediatric Neurological Disorders (10 papers), Medical Image Segmentation Techniques (8 papers) and Neonatal and fetal brain pathology (7 papers). Gerard Sanromà collaborates with scholars based in Spain, United States and South Korea. Gerard Sanromà's co-authors include Dinggang Shen, Guorong Wu, Gemma Piella, Francesc Serratosa, Miguel Á. González Ballester, René Alquézar, Oualid Benkarim, Yaozong Gao, E. Eixarch and Brent C. Munsell and has published in prestigious journals such as PLoS ONE, NeuroImage and Neurology.

In The Last Decade

Gerard Sanromà

28 papers receiving 504 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Gerard Sanromà 274 117 111 109 91 28 510
Bart Goossens 357 1.3× 91 0.8× 25 0.2× 206 1.9× 212 2.3× 23 660
Arnaldo Mayer 256 0.9× 107 0.9× 67 0.6× 222 2.0× 64 0.7× 45 584
Xiaolan Zeng 398 1.5× 65 0.6× 22 0.2× 192 1.8× 45 0.5× 9 596
Tassilo Klein 269 1.0× 160 1.4× 30 0.3× 214 2.0× 83 0.9× 28 632
Kola Babalola 210 0.8× 35 0.3× 25 0.2× 130 1.2× 51 0.6× 18 394
Hervé Lombaert 432 1.6× 118 1.0× 35 0.3× 351 3.2× 34 0.4× 40 843
Seyed Sadegh Mohseni Salehi 249 0.9× 173 1.5× 126 1.1× 267 2.4× 113 1.2× 15 628
Ana I. L. Namburete 138 0.5× 205 1.8× 218 2.0× 216 2.0× 42 0.5× 41 717
Andrea U. J. Mewes 417 1.5× 150 1.3× 199 1.8× 278 2.6× 70 0.8× 8 878
Ewout Vansteenkiste 216 0.8× 64 0.5× 47 0.4× 179 1.6× 25 0.3× 53 499

Countries citing papers authored by Gerard Sanromà

Since Specialization
Citations

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

Fields of papers citing papers by Gerard Sanromà

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gerard Sanromà

This figure shows the co-authorship network connecting the top 25 collaborators of Gerard Sanromà. A scholar is included among the top collaborators of Gerard Sanromà 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 Gerard Sanromà. Gerard Sanromà 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.
Benkarim, Oualid, Gemma Piella, Islem Rekik, et al.. (2020). A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly. Medical Image Analysis. 64. 101750–101750. 7 indexed citations
2.
Benkarim, Oualid, Michaël Aertsen, M. Pérez‐Cruz, et al.. (2019). Global and Regional Changes in Cortical Development Assessed by MRI in Fetuses with Isolated Nonsevere Ventriculomegaly Correlate with Neonatal Neurobehavior. American Journal of Neuroradiology. 40(9). 1567–1574. 14 indexed citations
3.
Sanromà, Gerard, et al.. (2019). Revealing heterogeneity of brain imaging phenotypes in Alzheimer’s disease based on unsupervised clustering of blood marker profiles. PLoS ONE. 14(3). e0211121–e0211121. 10 indexed citations
4.
Xia, Jing, Caiming Zhang, Fan Wang, et al.. (2018). Fetal cortical parcellation based on growth patterns. PubMed. 2018. 696–699. 2 indexed citations
5.
Benkarim, Oualid, et al.. (2018). Patch spaces and fusion strategies in patch-based label fusion. Computerized Medical Imaging and Graphics. 71. 79–89. 2 indexed citations
6.
Benkarim, Oualid, Gerard Sanromà, Gemma Piella, et al.. (2018). Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding. Lecture notes in computer science. 11072. 620–627. 2 indexed citations
7.
Sanromà, Gerard, Oualid Benkarim, Gemma Piella, et al.. (2018). Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation. Computerized Medical Imaging and Graphics. 69. 52–59. 14 indexed citations
8.
Benkarim, Oualid, Gemma Piella, E. Gratacós, et al.. (2018). Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly. NeuroImage Clinical. 18. 103–114. 23 indexed citations
9.
Sanromà, Gerard, Oualid Benkarim, Gemma Piella, et al.. (2017). Learning non-linear patch embeddings with neural networks for label fusion. Medical Image Analysis. 44. 143–155. 18 indexed citations
10.
Benkarim, Oualid, Gemma Piella, Miguel Á. González Ballester, & Gerard Sanromà. (2017). Discriminative confidence estimation for probabilistic multi-atlas label fusion. Medical Image Analysis. 42. 274–287. 8 indexed citations
11.
Zimmer, Veronika A., Ben Glocker, E. Eixarch, et al.. (2017). Learning and combining image neighborhoods using random forests for neonatal brain disease classification. Medical Image Analysis. 42. 189–199. 11 indexed citations
12.
Sanromà, Gerard, et al.. (2017). Patch-Based Techniques in Medical Imaging. Lecture notes in computer science. 8 indexed citations
13.
Sanromà, Gerard, Guorong Wu, Yaozong Gao, et al.. (2015). A transversal approach for patch-based label fusion via matrix completion. Medical Image Analysis. 24(1). 135–148. 21 indexed citations
14.
Du, Shaoyi, Yanrong Guo, Gerard Sanromà, et al.. (2015). Building dynamic population graph for accurate correspondence detection. Medical Image Analysis. 26(1). 256–267. 42 indexed citations
15.
Wu, Guorong, Minjeong Kim, Gerard Sanromà, et al.. (2014). Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. NeuroImage. 106. 34–46. 72 indexed citations
16.
Sanromà, Gerard, Guorong Wu, Yaozong Gao, & Dinggang Shen. (2014). Correction to “Learning to Rank Atlases for Multiple-Atlas Segmentation” [Oct 14 1939-1953]. IEEE Transactions on Medical Imaging. 33(11). 2210–2210. 1 indexed citations
17.
Burghouts, Gertjan J., Klamer Schutte, Sebastiaan P. van den Broek, et al.. (2014). Instantaneous threat detection based on a semantic representation of activities, zones and trajectories. Signal Image and Video Processing. 8(S1). 191–200. 19 indexed citations
18.
Sanromà, Gerard, Guorong Wu, Yaozong Gao, & Dinggang Shen. (2014). Learning to Rank Atlases for Multiple-Atlas Segmentation. IEEE Transactions on Medical Imaging. 33(10). 1939–1953. 45 indexed citations
19.
Sanromà, Gerard, René Alquézar, & Francesc Serratosa. (2010). A Discrete Labelling Approach to Attributed Graph Matching Using SIFT Features. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 954–957. 1 indexed citations
20.
Serratosa, Francesc & Gerard Sanromà. (2008). A FAST APPROXIMATION OF THE EARTH-MOVERS DISTANCE BETWEEN MULTIDIMENSIONAL HISTOGRAMS. International Journal of Pattern Recognition and Artificial Intelligence. 22(8). 1539–1558. 4 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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