부산대학교 도서관

We present a deep learning approach for nuclei segmentation at scale. Our algorithm aims to address the challenge of segmentation in dense scenes with limited annotated data available. Annotation in this domain is highly manual in nature, requiring time-consuming markup of the neuron and extensive expertise, and often results in errors. For these reasons, the approach under consideration employs methods adopted from transfer learning. This approach can also be extended to segment other components of the neurons.