Shigeru Nawano

3.1k total citations
89 papers, 2.2k citations indexed

About

Shigeru Nawano is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Oncology. According to data from OpenAlex, Shigeru Nawano has authored 89 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Radiology, Nuclear Medicine and Imaging, 31 papers in Computer Vision and Pattern Recognition and 27 papers in Oncology. Recurrent topics in Shigeru Nawano's work include Medical Image Segmentation Techniques (28 papers), Radiomics and Machine Learning in Medical Imaging (18 papers) and AI in cancer detection (18 papers). Shigeru Nawano is often cited by papers focused on Medical Image Segmentation Techniques (28 papers), Radiomics and Machine Learning in Medical Imaging (18 papers) and AI in cancer detection (18 papers). Shigeru Nawano collaborates with scholars based in Japan, United States and United Kingdom. Shigeru Nawano's co-authors include Katsuhiro Nasu, Akinobu Shimizu, Hidefumi Kobatake, Yoshifumi Kuroki, Seiko Kuroki, Koji Murakami, Seiji Yamamoto, Ken Motoori, Takuya Ueda and Tatsuaki Tsukamoto and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Radiology.

In The Last Decade

Shigeru Nawano

85 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shigeru Nawano Japan 23 1.1k 646 423 413 328 89 2.2k
Sergi Ganau Spain 14 682 0.6× 210 0.3× 176 0.4× 269 0.7× 315 1.0× 49 1.7k
Melcior Sentís Spain 19 757 0.7× 171 0.3× 218 0.5× 306 0.7× 129 0.4× 61 1.6k
Kazunori Kubota Japan 22 876 0.8× 206 0.3× 148 0.3× 85 0.2× 197 0.6× 102 1.5k
Jeon‐Hor Chen Taiwan 34 2.4k 2.2× 337 0.5× 428 1.0× 297 0.7× 157 0.5× 128 3.5k
Carolyn Mies United States 36 820 0.8× 1.3k 2.0× 630 1.5× 99 0.2× 128 0.4× 79 3.3k
Choon Hua Thng Singapore 32 965 0.9× 794 1.2× 279 0.7× 77 0.2× 400 1.2× 125 2.8k
N. Moriyama Japan 26 1.1k 1.0× 394 0.6× 179 0.4× 140 0.3× 669 2.0× 93 3.0k
Eriko Tohno Japan 21 1.6k 1.4× 519 0.8× 598 1.4× 44 0.1× 249 0.8× 85 3.0k
Amber L. Simpson United States 29 1.1k 1.0× 693 1.1× 39 0.1× 425 1.0× 203 0.6× 123 2.2k

Countries citing papers authored by Shigeru Nawano

Since Specialization
Citations

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

Fields of papers citing papers by Shigeru Nawano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shigeru Nawano

This figure shows the co-authorship network connecting the top 25 collaborators of Shigeru Nawano. A scholar is included among the top collaborators of Shigeru Nawano 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 Shigeru Nawano. Shigeru Nawano 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.
Ikeda, Yusuke, et al.. (2021). Lesion Image Generation Using Conditional GAN for Metastatic Liver Cancer Detection. Journal of Image and Graphics. 9(1). 27–30. 13 indexed citations
2.
Yamamoto, Seiji, et al.. (2016). Automated liver segmentation from a postmortem CT scan based on a statistical shape model. International Journal of Computer Assisted Radiology and Surgery. 12(2). 205–221. 11 indexed citations
3.
Watanabe, Hidefumi, et al.. (2013). Semi-automated organ segmentation using 3-dimensional medical imagery through sparse representation. 51(5). 300–312. 6 indexed citations
4.
Watanabe, Takashi, Kensei Tobinai, Taro Shibata, et al.. (2011). Phase II/III Study of R-CHOP-21 Versus R-CHOP-14 for Untreated Indolent B-Cell Non-Hodgkin's Lymphoma: JCOG 0203 Trial. Journal of Clinical Oncology. 29(30). 3990–3998. 45 indexed citations
5.
Shimizu, Akinobu, et al.. (2009). Pancreas segmentation from three dimensional contrast enhanced CT images based on a patient specific atlas. IEICE Technical Report; IEICE Tech. Rep.. 108(385). 181–186. 1 indexed citations
6.
Tobinai, Kensei, Takashi Watanabe, Michinori Ogura, et al.. (2009). Japanese phase II study of 90Y‐ibritumomab tiuxetan in patients with relapsed or refractory indolent B‐cell lymphoma. Cancer Science. 100(1). 158–164. 24 indexed citations
7.
Tobinai, Kensei, Michinori Ogura, Kuniaki Itoh, et al.. (2009). Phase II study of oral fludarabine in combination with rituximab for relapsed indolent B‐cell non‐Hodgkin lymphoma. Cancer Science. 100(10). 1951–1956. 7 indexed citations
8.
Kazama, Toshiki, Katsuhiro Nasu, Yoshifumi Kuroki, Shigeru Nawano, & Hisao Ito. (2009). Comparison of diffusion-weighted images using short inversion time inversion recovery or chemical shift selective pulse as fat suppression in patients with breast cancer. Japanese Journal of Radiology. 27(4). 163–167. 23 indexed citations
9.
Oda, Masahiro, Takayuki Kitasaka, Kensaku Mori, et al.. (2008). Digital bowel cleansing free detection method of colonic polyp from fecal tagging CT images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6915. 69152H–69152H. 2 indexed citations
10.
Yao, Cong, et al.. (2006). Simultaneous location detection of multi-organ by atlas-guided eigen-organ method in volumetric medical images. International Journal of Computer Assisted Radiology and Surgery. 1. 42–45. 11 indexed citations
11.
Nakamura, Yoshihiko, et al.. (2006). A study on blood vessel segmentation and lymph node detection from 3D abdominal X-ray CT images. International Journal of Computer Assisted Radiology and Surgery. 1. 381–382. 2 indexed citations
12.
Smutek, Daniel, et al.. (2006). Texture analysis of hepatocellular carcinoma and liver cysts in CT images. International Conference on Signal Processing. 56–59. 1 indexed citations
13.
Yao, Cong, et al.. (2006). Probabilistic Atlas-guided Eigen-organ Method for Simultaneous Bounding Box Estimation of Multiple Organs in Volumetric CT Images. 24(3). 191–200. 2 indexed citations
14.
Smutek, Daniel, et al.. (2006). Artificial Intelligence Methods Application in Liver Diseases Classification from CT Images. 146–155. 1 indexed citations
15.
Yao, Cong, et al.. (2005). Proposal of Atlas-guided Eigen-organ Method for Location Detection of Multi-Organs in Three Dimensional Medical Images(Joint Session 2). 105(303). 97–102. 2 indexed citations
16.
Kuroki, Seiko, Katsuhiro Nasu, Koji Murakami, et al.. (2004). Thymic MALT lymphoma. Clinical Imaging. 28(4). 274–277. 16 indexed citations
17.
Kobatake, Hidefumi, et al.. (2000). Advanced Computer-aided Detection System for Microcalcifications on Mammograms by Improvement of the Morphological Filter. 18(6). 795–804. 1 indexed citations
19.
Kobatake, Hidefumi, et al.. (1996). Development of an Adaptive System for Microcalcification Detection. 14(6). 699–706. 3 indexed citations
20.
Anzai, Yoshimi, Shigeru Nawano, Jun Itami, et al.. (1988). [MR imaging of parotid masses].. PubMed. 33(12). 1537–41. 1 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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