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Automated Training of Deep Convolutional Neural Networks for Cell Segmentation
Sajith Kecheril Sadanandan1, Petter Ranefall1, Sylvie Le Guyader2
1Department of Information Technology, Uppsala University, Sweden and SciLifeLab, Uppsala, Sweden.
Scientific Reports
|August 12, 2017
Summary
Deep Convolutional Neural Networks (DCNNs) achieve high performance in image segmentation. Training DCNNs with automatically generated cell-based ground truth yields results comparable to manual annotation, reducing data acquisition time.
Area of Science:
- Computer Vision
- Biomedical Imaging
- Machine Learning
Background:
- Deep Convolutional Neural Networks (DCNNs) are advanced algorithms for image segmentation.
- High-performance DCNNs require extensive, problem-specific training datasets.
- Manual annotation of training data is time-consuming and labor-intensive.
Purpose of the Study:
- To investigate the efficacy of automatically generated ground truth for training DCNNs.
- To compare the performance of DCNNs trained on automated versus manual annotations.
- To reduce the burden of manual data labeling in DCNN-based image segmentation.
Main Methods:
- Utilized fluorescently labeled cells to create automated ground truth data.
- Trained Deep Convolutional Neural Networks using this automatically generated dataset.
- Evaluated DCNN performance against models trained with manually annotated data.
Main Results:
- DCNNs trained with automatically generated ground truth demonstrated performance comparable to those trained with manual annotations.
- Automated annotation significantly reduced the time and resources required for dataset creation.
- The findings suggest a viable alternative to manual labeling for DCNN training.
Conclusions:
- Automatic ground truth generation using fluorescently labeled cells is an effective strategy for training DCNNs in image segmentation.
- This approach offers a scalable and efficient method for developing high-performing DCNN models.
- Reduces dependency on manual annotation, accelerating research and application development.
