Abstract
Deep learning-based methods play an increasingly important role in bioimage analysis. User-friendly tools are crucial for increasing the adoption of deep learning models and efforts have been made to support them in existing image analysis platforms. Due to hardware and software complexities, many of them have been struggling to support re-training and fine-tuning of models which is essential to avoid overfitting and hallucination issues when working with limited training data. Meanwhile, interactive machine learning provides an efficient way to train models on limited training data. It works by gradually adding new annotations by correcting the model predictions while the model is training in the background. In this work, we developed an ImJoy plugin for interactive training and an annotation tool for image segmentation. With a small example dataset obtained from the Human Protein Atlas, we demonstrate that CellPose-based segmentation models can be trained interactively from scratch within 10-40 minutes, which is at least 6x faster than the conventional annotation workflow and less labor intensive. We envision that the developed tool can make deep learning segmentation methods incrementally adoptable for new users and be used in a wide range of applications for biomedical image segmentation.
Highlights
Deep learning-based methods have been widely used to analyze biomedical images for common tasks such as segmentation[1,2], denoising[3,4] and classification[5]
We demonstrate a tool we built with ImJoy for interactive deep learning-based image segmentation
We demonstrated an interactive annotation and training tool that is capable of accelerating the annotation process for image segmentation
Summary
Deep learning-based methods have been widely used to analyze biomedical images for common tasks such as segmentation[1,2], denoising[3,4] and classification[5] Despite their potential, building user-friendly deep learning tools and distributing them to non-experts remains challenging. Depending on the training data distribution and many other factors, applying pre-trained deep learning models can suffer from overfitting or other generalization problems. This makes them vulnerable to subtle changes such as noise patterns generated by different microscopes, or morphological changes from different samples. It is required to re-train or fine-tune models with a user’s own data
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