Zhoubing Xu

2.0k total citations
37 papers, 976 citations indexed

About

Zhoubing Xu is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Zhoubing Xu has authored 37 papers receiving a total of 976 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Computer Vision and Pattern Recognition and 15 papers in Biomedical Engineering. Recurrent topics in Zhoubing Xu's work include Medical Image Segmentation Techniques (12 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Medical Imaging and Analysis (10 papers). Zhoubing Xu is often cited by papers focused on Medical Image Segmentation Techniques (12 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Medical Imaging and Analysis (10 papers). Zhoubing Xu collaborates with scholars based in United States, Germany and United Kingdom. Zhoubing Xu's co-authors include Bennett A. Landman, Richard G. Abramson, Yuankai Huo, Shunxing Bao, Albert Assad, Camilo Bermudez, Benjamin K. Poulose, Prasanna Parvathaneni, Rebeccah B. Baucom and Katherine S. Aboud and has published in prestigious journals such as PLoS ONE, NeuroImage and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Zhoubing Xu

37 papers receiving 960 citations

Peers

Zhoubing Xu

RNMI

CVPR

PRM

Ester Bonmati United Kingdom

Kelei He China

Alfiia Galimzianova United States

Fabian Isensee Germany

Yusong Lin China

Hidetaka Arimura Japan

Roger Trullo United States

Xiaowei Ding China

Soumya Ghose United States

Ester Bonmati United Kingdom

Zhoubing Xu

564 ×0.9

RNMI

480 ×1.2

CVPR

204 ×0.9

286 ×1.3

139 ×0.9

PRM

Citations per year, relative to Zhoubing Xu Zhoubing Xu (= 1×) peers Ester Bonmati

Countries citing papers authored by Zhoubing Xu

Since Specialization

Citations

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

Fields of papers citing papers by Zhoubing Xu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhoubing Xu

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

All Works

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20 of 20 papers shown

Xu, Zhoubing, et al.. (2024). COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training. IEEE Transactions on Medical Imaging. 43(5). 1995–2009. 15 indexed citations

Cai, Leon Y., Riqiang Gao, Ho Hin Lee, et al.. (2023). UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation. Medical Image Analysis. 90. 102939–102939. 51 indexed citations

Marschner, Sebastian, Manasi Datar, Zhoubing Xu, et al.. (2022). A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation. Radiation Oncology. 17(1). 129–129. 15 indexed citations

Tang, Yucheng, Riqiang Gao, Ho Hin Lee, et al.. (2021). Renal cortex, medulla and pelvicaliceal system segmentation on arterial phase CT images with random patch-based networks. PubMed. 11596. 42–42. 7 indexed citations

Seifert, Robert, Ken Herrmann, Jens Kleesiek, et al.. (2020). Semiautomatically Quantified Tumor Volume Using ⁶⁸Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer. Journal of Nuclear Medicine. 61(12). 1786–1792. 91 indexed citations

Chaganti, Shikha, Philippe Greniér, Guillaume Chabin, et al.. (2020). Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT. Radiology Artificial Intelligence. 2(4). e200048–e200048. 94 indexed citations

Huo, Yuankai, Zhoubing Xu, Katherine S. Aboud, et al.. (2019). 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage. 194. 105–119. 185 indexed citations

Huo, Yuankai, Zhoubing Xu, Shunxing Bao, et al.. (2018). Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks. PubMed. 10574. 8–8. 40 indexed citations

Xu, Zhoubing, et al.. (2016). Improving Spleen Volume Estimation Via Computer-assisted Segmentation on Clinically Acquired CT Scans. Academic Radiology. 23(10). 1214–1220. 10 indexed citations

10.

Xu, Zhoubing, et al.. (2015). Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9413. 94131N–94131N. 13 indexed citations

11.

Xu, Zhoubing, et al.. (2015). Efficient abdominal segmentation on clinically acquired CT with SIMPLE context learning. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9413. 94130L–94130L. 4 indexed citations

12.

Xu, Zhoubing, et al.. (2015). Multi-atlas segmentation for abdominal organs with Gaussian mixture models. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9417. 941707–941707. 6 indexed citations

13.

Xu, Zhoubing, Andrew J. Asman, Rebeccah B. Baucom, et al.. (2015). Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol. PLoS ONE. 10(10). e0141671–e0141671. 7 indexed citations

14.

Xu, Zhoubing, et al.. (2014). Self‐assessed performance improves statistical fusion of image labels. Medical Physics. 41(3). 31903–31903. 2 indexed citations

15.

Xu, Zhoubing, et al.. (2013). Texture analysis improves level set segmentation of the anterior abdominal wall. Medical Physics. 40(12). 121901–121901. 7 indexed citations

16.

Xu, Zhoubing, et al.. (2013). Automatic segmentation of abdominal wall in ventral hernia CT: a pilot study. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8669. 86693T–86693T. 2 indexed citations

17.

Xu, Zhoubing, Bo Li, Rebeccah B. Baucom, et al.. (2013). Immersive virtual reality for visualization of abdominal CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8673. 22 indexed citations

18.

Xu, Zhoubing, et al.. (2013). Quantitative anatomical labeling of the anterior abdominal wall. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8673. 867312–867312. 5 indexed citations

19.

Xu, Zhoubing, et al.. (2012). Segmentation of malignant gliomas through remote collaboration and statistical fusion. Medical Physics. 39(10). 5981–5989. 1 indexed citations

20.

Xu, Zhoubing, et al.. (2012). Collaborative labeling of malignant glioma. PubMed. 42. 1148–1151. 3 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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