Mark J. Gooding

2.4k total citations · 1 hit paper
64 papers, 1.6k citations indexed

About

Mark J. Gooding is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Biomedical Engineering. According to data from OpenAlex, Mark J. Gooding has authored 64 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Radiology, Nuclear Medicine and Imaging, 33 papers in Radiation and 13 papers in Biomedical Engineering. Recurrent topics in Mark J. Gooding's work include Advanced Radiotherapy Techniques (32 papers), Radiomics and Machine Learning in Medical Imaging (23 papers) and Medical Imaging Techniques and Applications (14 papers). Mark J. Gooding is often cited by papers focused on Advanced Radiotherapy Techniques (32 papers), Radiomics and Machine Learning in Medical Imaging (23 papers) and Medical Imaging Techniques and Applications (14 papers). Mark J. Gooding collaborates with scholars based in United Kingdom, Netherlands and United States. Mark J. Gooding's co-authors include Wouter van Elmpt, Paul Aljabar, Devis Peressutti, André Dekker, Johan van Soest, Tim Lustberg, J. van der Stoep, Eleanor Stride, Katherine A. Vallis and Charlotte L. Brouwer and has published in prestigious journals such as American Journal of Respiratory and Critical Care Medicine, International Journal of Radiation Oncology*Biology*Physics and IEEE Transactions on Medical Imaging.

In The Last Decade

Mark J. Gooding

62 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

2017 263 citations Tim Lustberg, Johan van Soest et al. Radiotherapy and Oncology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark J. Gooding United Kingdom	21	1.1k	781	453	378	252	64	1.6k
Bulat Ibragimov Denmark	26	1.2k 1.1×	416 0.5×	913 2.0×	314 0.8×	533 2.1×	76	2.5k
Liyuan Chen China	22	833 0.7×	423 0.5×	376 0.8×	321 0.8×	266 1.1×	89	1.5k
Pretesh Patel United States	33	1.4k 1.2×	949 1.2×	636 1.4×	1.3k 3.4×	274 1.1×	180	3.1k
Annette Haworth Australia	28	1.4k 1.2×	1.5k 1.9×	387 0.9×	1.4k 3.6×	230 0.9×	143	2.7k
Hossein Arabi Switzerland	35	2.4k 2.2×	516 0.7×	1.2k 2.6×	350 0.9×	277 1.1×	140	2.8k
Neelam Tyagi United States	23	1.2k 1.0×	900 1.2×	290 0.6×	551 1.5×	133 0.5×	82	1.6k
Linghong Zhou China	19	854 0.8×	580 0.7×	421 0.9×	307 0.8×	167 0.7×	71	1.2k
Yong Yin China	21	1.1k 1.0×	608 0.8×	255 0.6×	799 2.1×	145 0.6×	195	1.8k
Gilmer Valdés United States	24	1.3k 1.2×	880 1.1×	604 1.3×	490 1.3×	348 1.4×	59	2.0k
Hidetaka Arimura Japan	19	819 0.7×	181 0.2×	223 0.5×	547 1.4×	245 1.0×	123	1.3k

Countries citing papers authored by Mark J. Gooding

Since Specialization

Citations

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

Fields of papers citing papers by Mark J. Gooding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark J. Gooding. 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 Mark J. Gooding. The network helps show where Mark J. Gooding may publish in the future.

Co-authorship network of co-authors of Mark J. Gooding

This figure shows the co-authorship network connecting the top 25 collaborators of Mark J. Gooding. A scholar is included among the top collaborators of Mark J. Gooding 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 Mark J. Gooding. Mark J. Gooding 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

Gooding, Mark J., et al.. (2024). 654: Is full automation of radiotherapy treatment planning from scan to plan possible?. Radiotherapy and Oncology. 194. S2888–S2890. 1 indexed citations

Gooding, Mark J., Shafak Aluwini, Teresa Guerrero Urbano, et al.. (2024). Fully automated radiotherapy treatment planning: A scan to plan challenge. Radiotherapy and Oncology. 200. 110513–110513. 5 indexed citations

Spezi, Emiliano, et al.. (2023). Clinical evaluation of atlas-based auto-segmentation in breast and nodal radiotherapy. British Journal of Radiology. 96(1149). 20230040–20230040. 4 indexed citations

Boukerroui, Djamal, et al.. (2023). An approach to generate synthetic 4DCT datasets to benchmark Mid-Position implementations. Physica Medica. 114. 103144–103144. 1 indexed citations

Zegers, Catharina M.L., Richard Canters, Mark J. Gooding, et al.. (2023). Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology. Physica Medica. 114. 103156–103156. 1 indexed citations

Boukerroui, Djamal, Eliana Vásquez Osorio, Ellen Brunenberg, & Mark J. Gooding. (2023). Analytic calculations and synthetic shapes for validation of quantitative contour comparison software. Physics and Imaging in Radiation Oncology. 26. 100436–100436. 2 indexed citations

Claessens, Michaël, Verdi Vanreusel, Fredrik Löfman, et al.. (2022). Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm. Physics in Medicine and Biology. 67(11). 115014–115014. 20 indexed citations

Coudyzer, Walter, Jeroen Bertels, David Robben, et al.. (2020). Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. European Journal of Nuclear Medicine and Molecular Imaging. 47(12). 2742–2752. 47 indexed citations

Dijk, Lisanne V. van, L. Van den Bosch, Paul Aljabar, et al.. (2019). Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring. Radiotherapy and Oncology. 142. 115–123. 153 indexed citations

10.

Boukerroui, Djamal, Devis Peressutti, Johan van Soest, et al.. (2019). An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning. IEEE Transactions on Medical Imaging. 38(11). 2654–2664. 25 indexed citations

11.

Hazelaar, Colien, Ana Vaniqui, Mark J. Gooding, et al.. (2019). Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy. Physics and Imaging in Radiation Oncology. 13. 1–6. 127 indexed citations

12.

Boukerroui, Djamal, Devis Peressutti, Johan van Soest, et al.. (2018). Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory. IEEE Transactions on Medical Imaging. 38(1). 99–106. 22 indexed citations

13.

Talwar, Arunabh, Fergus Gleeson, Najib M. Rahman, et al.. (2015). Validation Of 4 Models To Estimate The Probability Of Malignancy In Patients With Sub-Centimeter Pulmonary Nodules. American Journal of Respiratory and Critical Care Medicine. 191. 1 indexed citations

14.

Gooding, Mark J., et al.. (2010). Three-Dimensional Ultrasound Imaging of Mammary Ducts in Lactating Women. Journal of Ultrasound in Medicine. 29(1). 95–103. 13 indexed citations

15.

Gooding, Mark J., et al.. (2010). Investigation into the Fusion of Multiple 4-D Fetal Echocardiography Images to Improve Image Quality. Ultrasound in Medicine & Biology. 36(6). 957–966. 23 indexed citations

16.

Barber, Thomas M., Mark J. Gooding, Rachel J. Smith, et al.. (2009). Patterns of ovarian morphology in polycystic ovary syndrome: a study utilising magnetic resonance imaging. European Radiology. 20(5). 1207–1213. 31 indexed citations

17.

Gooding, Mark J., Stephen Kennedy, & J. Alison Noble. (2007). Volume Segmentation and Reconstruction from Freehand Three-Dimensional Ultrasound Data with Application to Ovarian Follicle Measurement. Ultrasound in Medicine & Biology. 34(2). 183–195. 14 indexed citations

18.

Gooding, Mark J., et al.. (2005). Automatic Mammary Duct Detection in 3D Ultrasound. Lecture notes in computer science. 8(Pt 1). 434–441. 5 indexed citations

19.

Gooding, Mark J., D C Barber, Stephen Kennedy, & J. Alison Noble. (2004). Measurement of the speed of sound in follicular fluid. Human Reproduction. 20(2). 497–500. 2 indexed citations

20.

Gooding, Mark J., et al.. (1978). Evidence of a uteroglobin-like protein in epithelial cells of reproductive and non-reproductive tissues of the rabbit. Reproduction. 54(1). 193–196. 23 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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