MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images
| Autor: | Shan E Ahmed Raza, David B. A. Epstein, Michael Khan, Stella Pelengaris, Nasir M. Rajpoot, Linda Cheung |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Cell segmentation Context (language use) Image segmentation Convolutional neural network Convolution 03 medical and health sciences 030104 developmental biology Segmentation Computer vision Deconvolution Artificial intelligence business |
| Zdroj: | ISBI |
| DOI: | 10.1109/isbi.2017.7950532 |
| Popis: | We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows us to deal with variable intensity cell boundaries and highly variable cell size in the mouse pancreatic tissue by adding extra convolutional layers which bypass the max-pooling operation. The results show that our method outperforms state-of-the-art deep learning based approaches for segmentation. |
| Databáze: | OpenAIRE |
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