Biology subjects

Geuther, B. Q.

Publications and source records attributed to Geuther, B. Q..

5 recordsLinked to original sources

bioRxiv2026

Optimizing Intermediate Representations: A Framework for Low-Cost, High-Accuracy Behavior Quantification

Quantitative measurement of animal behavior is a cornerstone of neuroscience, genetics, and ethology. While modern computer vision has democratized automated analysis, the field has coalesced around pose estimation as the standard intermediate representation. This reliance imposes a significant bottleneck: researchers must often train custom pose models using large, labor-intensive datasets. Furthermore, the assumption that denser anatomical tracking yields better classification remains largely unverified. Here, we benchmark intermediate representations for supervised mouse behavior classification to determine the optimal trade-off between annotation cost and model performance. We systematically evaluate the sensitivity of classification to keypoint density, the impact of temporal feature engineering, and the viability of segmentation-derived shape descriptors as a low-cost alternative. We find that classifier performance is remarkably robust to keypoint variation; increasing keypoint density yields negligible gains, particularly when behavior training sets are sufficiently large. In contrast, augmenting models with temporal features (specifically FFT-based signal processing) consistently drives performance improvements. Crucially, we demonstrate that whole-body segmentation achieves performance parity with explicit pose estimation across most behaviors. These findings challenge the "more is better" intuition in pose tracking and suggest a paradigm shift: efficient pipelines should prioritize behavioral dataset volume and temporal dynamics over complex anatomical keypoints.

neuroscience↗

bioRxiv2023

Highly Accurate and Precise Determination of Mouse Mass Using Computer Vision

1Changes in body mass are a key indicator of health and disease in humans and model organisms. Animal body mass is routinely monitored in husbandry and preclinical studies. In rodent studies, the current best method requires manually weighing the animal on a balance which has at least two consequences. First, direct handling of the animal induces stress and can have confounding effects on studies. Second, the acquired mass is static and not amenable to continuous assessment, and rapid mass changes can be missed. A noninvasive and continuous method of monitoring animal mass would have utility in multiple areas of biomedical research. Here, we test the feasibility of determining mouse body mass using video data. We combine computer vision methods with statistical modeling to demonstrate the feasibility of our approach. Our methods determine mouse mass with 4.8% error across highly genetically diverse mouse strains, with varied coat colors and mass. This error is low enough to replace manual weighing with image-based assessment in most mouse studies. We conclude that visual determination of rodent mass using video enables noninvasive and continuous monitoring and can improve animal welfare and preclinical studies.

genetics↗

bioRxiv2022

Video based phenotyping platform for the laboratory mouse

Automated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated behavior detection and allow high-throughput preclinical and mechanistic studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational laboratories. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher-level constructs such as biological age and pain. Here, we present our, integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (ethograph) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. This open-source tool is an ecosystem that allows the neuroscience and genetics community for shared advanced behavior analysis and reduces the barrier to entry into this new field.

animal behavior and cognition↗

bioRxiv2020

Action detection using a neural network elucidates the genetics of mouse grooming behavior

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming, a prototypical stereotyped behavior, is often used as an endophenotype in psychiatric genetics. Using mouse grooming behavior as an example, we develop a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operate across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We survey grooming behavior in the open field in 2500 mice across 62 strains, determine its heritable components, conduct GWAS to outline its genetic architecture, and perform PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of mechanisms underlying these behaviors.

bioinformatics↗

bioRxiv2018

Robust Mouse Tracking in Complex Environments using Neural Networks

The ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background, in order to achieve proper foreground/background detection (segmentation). However, as behavioral paradigms become more sophisticated with ethologically relevant environments, the approach of modifying environmental conditions offers diminishing returns, particularly for scalable experiments. Currently, there is a need for methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we developed a state-of-the-art neural network-based tracker for mice, using modern machine vision techniques. We test three different neural network architectures to determine their performance on genetically diverse mice under varying environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pre-trained network for the mouse behavior and neuroscience communities. This general-purpose neural network tracker can be easily extended to other experimental paradigms and even to other animals, through transfer learning, thus providing a robust, generalizable solution for biobehavioral research.

animal behavior and cognition↗