TY - GEN
T1 - Deep Learning Segmentation of Protein em Maps
AU - Zumbado-Corrales, Manuel
AU - Esquivel-Rodriguez, Juan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Protein Electron Microscopy (EM) maps are critical in determining the three-dimensional structures of bio-molecules, including proteins and their interactions. The task of identifying regions that correspond to specific proteins is challenging, but crucial for gaining insight into their function and designing drugs to enhance or suppress their processes. Conventional methods of protein EM map segmentation use algorithms that assign a voxel to a region, but can result in difficulties in obtaining a segmentation that maps each region to a single protein unit. Our approach incorporates an interactive mechanism that allows for user guidance of the segmentation process. Our deep learning model is trained on a dataset of protein EM maps and uses a U - Net architecture. Results show that our approach has potential to overcome limitations of traditional state-of-the-art approaches.
AB - Protein Electron Microscopy (EM) maps are critical in determining the three-dimensional structures of bio-molecules, including proteins and their interactions. The task of identifying regions that correspond to specific proteins is challenging, but crucial for gaining insight into their function and designing drugs to enhance or suppress their processes. Conventional methods of protein EM map segmentation use algorithms that assign a voxel to a region, but can result in difficulties in obtaining a segmentation that maps each region to a single protein unit. Our approach incorporates an interactive mechanism that allows for user guidance of the segmentation process. Our deep learning model is trained on a dataset of protein EM maps and uses a U - Net architecture. Results show that our approach has potential to overcome limitations of traditional state-of-the-art approaches.
KW - cryo-electron microscopy
KW - deep learning
KW - extreme points
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85184350838&partnerID=8YFLogxK
U2 - 10.1109/BIP60195.2023.10379274
DO - 10.1109/BIP60195.2023.10379274
M3 - Contribución a la conferencia
AN - SCOPUS:85184350838
T3 - 5th IEEE International Conference on BioInspired Processing, BIP 2023
BT - 5th IEEE International Conference on BioInspired Processing, BIP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on BioInspired Processing, BIP 2023
Y2 - 28 November 2023 through 30 November 2023
ER -