Cart
Create Account
Sign In
Already purchased this program?
Login to View
This video program is a part of the Premium package:
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation
- IEEE MemberUS $11.00
- Society MemberUS $0.00
- IEEE Student MemberUS $11.00
- Non-IEEE MemberUS $15.00
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation
The Pancreatic beta cell is an important target in diabetes research. For scalable modeling of beta cell ultastructure, we investigate automatic segmentation of whole cell imaging data acquired through soft X-ray tomography. During the course of the study, both complete and partial ultrastrucutre annotations were produced manually for different subsets of the data. To more effectively use existing annotations, we propose a method that enables the application of partially labeled data for full label segmentation. For experimental validation, we apply our method to train a convolutional neural network with a set of 12 fully annotated data and 12 partially annotated data and show promising improvement over standard training that uses fully annotated data alone.
The Pancreatic beta cell is an important target in diabetes research. For scalable modeling of beta cell ultastructure, we investigate automatic segmentation of whole cell imaging data acquired through soft X-ray tomography. During the course of the study, both complete and partial ultrastrucutre annotations were produced manually for different subsets of the data. To more effectively use existing annotations, we propose a method that enables the application of partially labeled data for full label segmentation. For experimental validation, we apply our method to train a convolutional neural network with a set of 12 fully annotated data and 12 partially annotated data and show promising improvement over standard training that uses fully annotated data alone.
