EM-net: Deep learning for electron microscopy image segmentation
Afshin Khadangi, Thomas Boudier, Vijay Rajagopal
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | IEEE COMPUTER SOC | Published : 2021
Recent high-throughput electron microscopy techniques such as focused ion-beam scanning electron microscopy (FIB-SEM) provide thousands of serial sections which assist the biologists in studying sub-cellular structures at high resolution and large volume. The low contrast of such images hinders image segmentation and 3D visualisation of these datasets. With recent advances in computer vision and deep learning, such datasets can be segmented and reconstructed in 3D with greater ease and speed than with previous approaches. However, these methods still rely on thousands of ground-truth samples for training and electron microscopy datasets require significant amounts of time for carefully curat..View full abstract
This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200.