Automatic Multi Class Organelle Segmentation For Cellular Fib-Sem Images

Focused Ion Beam milling combined with Scanning Electron Microscopy (FIB-SEM) technique is an electron microscopy imaging method that offers the possibility of acquiring 3D isotropic images of biological structures at the nanometric scale. Automated image segmentation is required for morphological analysis of huge image stacks and to save time consuming manual intervention. Current methods are either specific to data and organelles or lack accuracy. We propose a robust multi-class semantic segmentation method for FIBSEM images, based on deep neural networks. We evaluate and compare our proposed method on two FIB-SEM images, for the segmentation of mitochondria, cell membrane and endoplasmic reticulum. We achieve results close to inter-expert variability.