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Integrating XMALab and DeepLabCut for high-throughput XROMM.

J D Laurence-Chasen¹, Armita R Manafzadeh², Nicholas G Hatsopoulos³

¹Department of Organismal Biology and Anatomy, The University of Chicago, 1027 E 57th St, Chicago, IL 60637, USA jdlaurence@uchicago.edu fritziea@uchicago.edu.

The Journal of Experimental Biology

|July 16, 2020

Summary

This study introduces a new workflow combining DeepLabCut and XMALab for markerless tracking in X-ray reconstruction of moving morphology (XROMM). The integrated approach significantly increases data processing throughput for in vivo studies.

Keywords:

Deep learning DeepLabCut Marker tracking XMALab XROMM

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Area of Science:

Biomechanics
Computational Biology
Medical Imaging

Background:

Marker tracking in X-ray reconstruction of moving morphology (XROMM) presents a significant bottleneck.
Existing methods limit data processing throughput for large-scale studies.

Purpose of the Study:

To evaluate the efficacy of DeepLabCut, a markerless tracking tool, for improving XROMM data processing.
To develop and test a novel workflow integrating DeepLabCut with XMALab software.

Main Methods:

A new workflow was developed integrating DeepLabCut for markerless tracking with XMALab for training data generation, error correction, and 3D reconstruction.
The workflow was tested on two in vivo behavioral studies and one post-mortem manipulation study.

Main Results:

The integrated workflow demonstrated a 6 to 13-fold increase in data throughput for in vivo XROMM studies.
DeepLabCut's generalization was limited in a post-mortem study with novel poses, not surpassing XMALab alone.

Conclusions:

The novel workflow significantly enhances data throughput for XROMM, enabling larger scale studies.
DeepLabCut is effective for markerless tracking in XROMM when applied within its appropriate context, particularly for in vivo behaviors.