Multi-channel Deep Transfer Learning for Nuclei Segmentation in Glioblastoma Cell Tissue Images

Segmentation and quantification of cell nuclei is an important task in tissue microscopy image analysis. We introduce a deep learning method leveraging atrous spatial pyramid pooling for cell segmentation. We also present two different approaches for transfer learning using datasets with a different number of channels. A quantitative comparison with previous methods was performed on challenging glioblastoma cell tissue images. We found that our transfer learning method improves the segmentation result.