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Interactive biomedical segmentation tool powered by deep learning and ImJoy
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- Ouyang, Wei (author)
- KTH,Science for Life Laboratory, SciLifeLab,Cellulär och klinisk proteomik
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- Le, Trang (author)
- KTH,Science for Life Laboratory, SciLifeLab,Cellulär och klinisk proteomik
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- Xu, Hao (author)
- KTH,Science for Life Laboratory, SciLifeLab,Cellulär och klinisk proteomik,Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
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- (creator_code:org_t)
- 2021
- 2021
- English.
- In: F1000 Research. - : F1000 Research Ltd. - 2046-1402. ; 10
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- Deep Learning
- Image Analysis
- ImJoy
- Interactive Machine Learning
- Segmentation
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Lundberg, Emma
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