D
Dimitri Perrin
Researcher at Queensland University of Technology
Publications - 96
Citations - 3818
Dimitri Perrin is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Genome & Image registration. The author has an hindex of 17, co-authored 89 publications receiving 2891 citations. Previous affiliations of Dimitri Perrin include RIKEN Quantitative Biology Center & Osaka University.
Papers
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Journal ArticleDOI
Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational Analysis
Etsuo A. Susaki,Kazuki Tainaka,Kazuki Tainaka,Dimitri Perrin,Fumiaki Kishino,Takehiro Tawara,Tomonobu M. Watanabe,Chihiro Yokoyama,Hirotaka Onoe,Megumi Eguchi,Shun Yamaguchi,Shun Yamaguchi,Takaya Abe,Hiroshi Kiyonari,Yoshihiro Shimizu,Atsushi Miyawaki,Hideo Yokota,Hiroki R. Ueda +17 more
TL;DR: CUBIC enables time-course expression profiling of whole adult brains with single-cell resolution and develops a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline that enable the visualization and quantification of neural activities induced by environmental stimulation.
Journal ArticleDOI
Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging.
Etsuo A. Susaki,Etsuo A. Susaki,Etsuo A. Susaki,Kazuki Tainaka,Kazuki Tainaka,Kazuki Tainaka,Dimitri Perrin,Hiroko Yukinaga,Akihiro Kuno,Hiroki R. Ueda,Hiroki R. Ueda,Hiroki R. Ueda +11 more
TL;DR: A protocol for advanced CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis) is described in this paper, which enables simple and efficient organ clearing, rapid imaging by light-sheet microscopy and quantitative imaging analysis of multiple samples.
Journal ArticleDOI
Whole-body imaging with single-cell resolution by tissue decolorization.
Kazuki Tainaka,Shimpei I. Kubota,Takeru Q. Suyama,Etsuo A. Susaki,Etsuo A. Susaki,Dimitri Perrin,Maki Ukai-Tadenuma,Hideki Ukai,Hiroki R. Ueda,Hiroki R. Ueda +9 more
TL;DR: In this article, the authors show that an aminoalcohol decolorizes blood by efficiently eluting the heme chromophore from hemoglobin, which can be used to render nearly transparent almost all organs of adult mice.
Journal Article
Whole-Body Imaging with Single-Cell Resolution by Tissue Decolorization
Kazuki Tainaka,Shimpei I. Kubota,Takeru Q. Suyama,Etsuo A. Susaki,Dimitri Perrin,Maki Ukai-Tadenuma,Hideki Ukai,Hiroki R. Ueda +7 more
TL;DR: It is shown that an aminoalcohol decolorizes blood by efficiently eluting the heme chromophore from hemoglobin, suggesting that whole-body imaging of colorless tissues at high resolution will contribute to organism-level systems biology.
Journal ArticleDOI
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter,Diana S. M. Buist,Christoph I. Lee,Yaroslav Nikulin,Dezső Ribli,Yuanfang Guan,William Lotter,Zequn Jie,Hao Du,Sijia Wang,Jiashi Feng,Mengling Feng,Hyo-Eun Kim,F. Albiol,Alberto Albiol,Stephen Morrell,Zbigniew Wojna,Mehmet Eren Ahsen,Umar Asif,Antonio Jimeno Yepes,Shivanthan A.C. Yohanandan,Simona Rabinovici-Cohen,Darvin Yi,Bruce Hoff,Thomas Yu,Elias Chaibub Neto,Daniel L. Rubin,Peter Lindholm,Laurie R. Margolies,Russell B. McBride,Joseph H. Rothstein,Weiva Sieh,Rami Ben-Ari,Stefan Harrer,Andrew D. Trister,Stephen H. Friend,Thea Norman,Berkman Sahiner,Fredrik Strand,Fredrik Strand,Justin Guinney,Gustavo Stolovitzky,Lester Mackey,Joyce Cahoon,Li Shen,Jae Ho Sohn,Hari Trivedi,Yiqiu Shen,Ljubomir Buturovic,Jose Costa Pereira,Jaime S. Cardoso,Eduardo Castro,Karl Trygve Kalleberg,Obioma Pelka,Imane Nedjar,Krzysztof J. Geras,Felix Nensa,Ethan Goan,Sven Koitka,Sven Koitka,Luis Caballero,David D. Cox,Pavitra Krishnaswamy,Gaurav Pandey,Christoph M. Friedrich,Dimitri Perrin,Clinton Fookes,Bibo Shi,Gerard Cardoso Negrie,Michael Kawczynski,Kyunghyun Cho,Can Son Khoo,Joseph Y. Lo,A. Gregory Sorensen,Hwejin Jung +74 more
TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.