A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow

Marco Pérez Cáceres ¹, Alexandre Gauvin ², Félix Dumais ³

⁰Department of Radiology, University of Montreal, Montréal, Québec, Canada.

World Neurosurgery +

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June 12, 2024

View abstract on PubMed

Summary

This study presents a framework for accelerating clinical research using machine learning for image segmentation. It details methods to improve reproducibility and efficiency through iterative model training and expert validation.

Area Of Science

Neuroimaging
Machine Learning
Clinical Research

Background

Open-source tools are driving exponential growth in machine learning applications.
Machine learning integration, especially for neuroimaging segmentation, is becoming more accessible.

Purpose Of The Study

To present a generalized methodology for expediting and enhancing the reproducibility of clinical research.
To outline critical considerations for hardware, software, neural network training, and data labeling.

Main Methods

Advocates an iterative approach to model training and transfer learning.
Emphasizes internal validation and early outlier handling during the data labeling process.
Proposes fine-tuning models later in the development cycle.

Main Results

Iterative refinement allows expert intervention to improve model reliability.
Reduces manual work time for experts.
Enables seamless integration of model predictions for standardized, reproducible results.

Conclusions

Provides a comprehensive framework for accelerating research with machine learning for image segmentation.
Enhances efficiency and reliability in clinical research applications.

Keywords:

Deep learning Neuroimaging Segmentation