Online fat detection and evaluation in modelling digital physiological fish-Reference-Cited by-同舟云学术

Online fat detection and evaluation in modelling digital physiological fish

Published:2020-06-08 Issue:8 Volume:51 Page:3175-3190
ISSN:1355-557X
Container-title:Aquaculture Research
language:en
Short-container-title:Aquac Res

Author:

Nian Rui¹²^ORCID,Gao Mingshan¹,Kong Shuang¹,Yu Junjie¹,Wang Ruirui¹,Li Xueshan³,Zhang Shichang⁴,Hao Baochen⁵,Xu Xiao⁶,Che Renzheng⁷,Ai Qinghui³,Macq Benoit⁸

Affiliation:

1. School of Information Science and Engineering Ocean University of China Qingdao China

2. Radiology Department Harvard Medical School Boston MA USA

3. The Key Laboratory of Mariculture (Ministry of Education) Ocean University of China Qingdao China

4. Baidu Technology Park Beijing China

5. Boe Technology Group Co., Ltd. Beijing China

6. Hisense Group Co., Ltd. Qingdao China

7. Qingdao Research Institute Beijing University of Aeronautics and Astronautics Qingdao China

8. ICTEAM Institute Université catholique de Louvain Louvain‐la‐Neuve Belgium

Funder

National Program of International S&T Cooperation

National High-Tech R&D 863 Program

Natural Science Foundation of P.R. China

National Science & Technology Pillar Program

National Key R&D Program

Publisher

Wiley

Subject

Aquatic Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/are.14653

Reference43 articles.

1. Determination of fat content of burbot (Lota lota) liver with low field MR imaging (0.04 T)

2. The coelacanth rostral organ is a unique low-resolution electro-detector that facilitates the feeding strike

3. Chemical shift based MR imaging and gas chromatography for quantification and localization of fat in Atlantic mackerel

4. Big Data Deep Learning: Challenges and Perspectives

5. Rapid quantification of muscle fat content and subcutaneous adipose tissue in fish using MRI