CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images

2026-07-07 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Artificial Intelligence & Machine Learning, Health & Medical Research, Life Sciences & Biology · Depth: Intermediate, extended

Summary

CODAvision is a novel graphical user interface (GUI) designed to simplify deep learning-based semantic segmentation of medical images, making it accessible to scientists without extensive programming expertise. This tool provides step-by-step instructions for automatic segmentation, streamlining the creation of robust training datasets, configuring model parameters, and optimizing performance across diverse biomedical image modalities like histology, magnetic resonance imaging, and computed tomography. Built upon the CODA algorithm, CODAvision enhances usability by automating model training, performance evaluation, and generating quantitative results and comprehensive reports. It demonstrates robust performance, achieving over 90% overall accuracy and over 85% per-class precision and recall with a DeepLabv3+ architecture and ResNet50 backbone, for applications such as quantifying metastatic burden in in vivo models and deconvolution of spatial transcriptomics datasets.

Key takeaway

For research scientists and domain experts needing to perform medical image segmentation without deep coding skills, CODAvision offers a critical solution. You can rapidly design and train highly customizable deep learning models through its intuitive graphical interface, significantly accelerating your research workflows. This enables precise spatial quantification of microanatomical structures across various image modalities, allowing you to focus on biological questions rather than complex programming.

Key insights

CODAvision democratizes deep learning for medical image segmentation via an intuitive GUI, enabling non-programmers to train custom models.

Principles

• GUI-guided workflows enhance accessibility for complex ML tasks.
• Customizable deep learning models improve adaptability across modalities.
• Automated reporting and quantification streamline research analysis.

Method

The workflow involves manual annotation parameterization, automatic conversion to optimized training tiles, model configuration via GUI, training, and automated performance evaluation.

In practice

• Use CODAvision to quantify metastatic burden in histology images.
• Apply the tool for deconvolution of spatial transcriptomics datasets.
• Train custom segmentation models for MRI and CT scans.

Topics

• Medical Image Segmentation
• Deep Learning Models
• CODAvision
• Histology Analysis
• Spatial Transcriptomics
• Biomedical Imaging

Code references

• Kiemen-Lab/CODAvision

Best for: Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.