Back to Search
Start Over
GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation
- Source :
- Medical image analysis. 56
- Publication Year :
- 2018
-
Abstract
- Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks. In this work, we propose a new deep learning method for cell segmentation, which integrates convolutional neural networks and gated recurrent neural networks over multiple image scales to exploit the strength of both types of networks. To increase the robustness of the training and improve segmentation, we introduce a novel focal loss function. We also present a distributed scheme for optimized training of the integrated neural network. We applied our proposed method to challenging data of glioblastoma cell nuclei and performed a quantitative comparison with state-of-the-art methods. Insights on how our extensions affect training and inference are also provided. Moreover, we benchmarked our method using a wide spectrum of all 22 real microscopy datasets of the Cell Tracking Challenge.
- Subjects :
- Computer science
Cytological Techniques
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cell segmentation
Inference
Health Informatics
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Robustness (computer science)
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Microscopy
Models, Statistical
Radiological and Ultrasound Technology
Artificial neural network
business.industry
Deep learning
Pattern recognition
Computer Graphics and Computer-Aided Design
Recurrent neural network
Computer Vision and Pattern Recognition
Artificial intelligence
Neural Networks, Computer
business
Glioblastoma
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 13618423
- Volume :
- 56
- Database :
- OpenAIRE
- Journal :
- Medical image analysis
- Accession number :
- edsair.doi.dedup.....ce2cb32b39d65db4bdb7dd79b68a9e18