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使用计算机视觉技术高度准确且精确地测定小鼠体重。

Highly accurate and precise determination of mouse mass using computer vision.

作者信息

Guzman Malachy, Geuther Brian Q, Sabnis Gautam S, Kumar Vivek

机构信息

The Jackson Laboratory, Bar Harbor, ME, USA.

Carleton College, Northfield, MN, USA.

出版信息

Patterns (N Y). 2024 Aug 7;5(9):101039. doi: 10.1016/j.patter.2024.101039. eCollection 2024 Sep 13.


DOI:10.1016/j.patter.2024.101039
PMID:39568644
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11573914/
Abstract

Changes in body mass are key indicators of health in humans and animals and are routinely monitored in animal husbandry and preclinical studies. In rodent studies, the current method of manually weighing the animal on a balance causes at least two issues. First, directly handling the animal induces stress, possibly confounding studies. Second, these data are static, limiting continuous assessment and obscuring rapid changes. A non-invasive, continuous method of monitoring animal mass would have utility in multiple biomedical research areas. We combine computer vision with statistical modeling to demonstrate the feasibility of determining mouse body mass by using video data. Our methods determine mass with a 4.8% error across genetically diverse mouse strains with varied coat colors and masses. This error is low enough to replace manual weighing in most mouse studies. We conclude that visually determining rodent mass enables non-invasive, continuous monitoring, improving preclinical studies and animal welfare.

摘要

体重变化是人类和动物健康的关键指标,在畜牧业和临床前研究中经常进行监测。在啮齿动物研究中,目前在天平上手动称量动物体重的方法至少会引发两个问题。首先,直接处理动物会产生应激反应,可能会混淆研究结果。其次,这些数据是静态的,限制了连续评估并掩盖了快速变化。一种非侵入性的连续监测动物体重的方法将在多个生物医学研究领域具有实用价值。我们将计算机视觉与统计建模相结合,以证明通过使用视频数据确定小鼠体重的可行性。我们的方法在具有不同毛色和体重的多种基因不同的小鼠品系中确定体重时,误差为4.8%。这个误差足够低,可以在大多数小鼠研究中取代手动称重。我们得出结论,通过视觉确定啮齿动物体重能够实现非侵入性的连续监测,改善临床前研究和动物福利。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/058978f71483/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/b7cc169934b4/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/9ab498d7071f/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/1619e8085765/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/26a5af94323a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/a61f2053804a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/058978f71483/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/b7cc169934b4/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/9ab498d7071f/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/1619e8085765/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/26a5af94323a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/a61f2053804a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/afc8/11573914/058978f71483/gr6.jpg

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本文引用的文献

[1]
Towards foundation models of biological image segmentation.

Nat Methods. 2023-7

[2]
An Automated, Home-Cage, Video Monitoring-based Mouse Frailty Index Detects Age-associated Morbidity in C57BL/6 and Diversity Outbred Mice.

J Gerontol A Biol Sci Med Sci. 2023-5-11

[3]
Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation.

Cell Rep. 2022-1-11

[4]
High-throughput visual assessment of sleep stages in mice using machine learning.

Sleep. 2022-2-14

[5]
Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives.

Front Behav Neurosci. 2021-9-24

[6]
Estimating body weight of pigs from posture analysis using a depth camera.

Anim Sci J. 2021

[7]
The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals.

Animals (Basel). 2021-8-9

[8]
Weight and volume estimation of poultry and products based on computer vision systems: a review.

Poult Sci. 2021-5

[9]
Action detection using a neural network elucidates the genetics of mouse grooming behavior.

Elife. 2021-3-17

[10]
Image Segmentation Using Deep Learning: A Survey.

IEEE Trans Pattern Anal Mach Intell. 2022-7

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