EE Seminar: Learning from noise: data-driven processing of low SNR images

09 בדצמבר 2019, 15:00 
room 011, Kitot Building 

(The talk will be given in English)

 

Speaker:     Dr. Ayelet Heimowitz
                     Program for Applied and Computational Mathematics at Princeton University

Monday, December 9th, 2019
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

 

Learning from noise: data-driven processing of low SNR images

 

Abstract

Single-particle cryo-electron microscopy aims to determine the structure of 3D macromolecules from multiple 2D projections. The high levels of noise present in experimental data, in conjunction with a distortion applied by the electron microscope, is a cause of significant challenges to the process of acquiring particle stacks from the raw data outputted by the electron microscope. First and foremost, the noise and distortion complicate the selection of particle projections, making edge detection methods ineffective and adding many pitfalls to template-based methods. Furthermore, even after a successful particle picking, any 3D volume reconstructed from the distorted projections selected may yield an unreliable representation of the macromolecule. It is therefore necessary to estimate the distortion applied by the electron-microscope to each of the picked projection images.

 

In this talk I will introduce a simple and novel approach for fast, accurate, and template-free particle picking. This method includes no model for the particle, and instead focuses on prior knowledge on the noise. Furthermore, this method can be used in any application where the goal is to identify the location of several repeating patterns corrupted by high levels of uncorrelated noise. I will also discuss a novel, data-driven method for estimating the distortion applied by the electron-microscope. I will include an evaluation of these methods on several publicly available datasets.

Short Bio
Ayelet Heimowitz is a postdoctoral research associate in the Program for Applied and Computational Mathematics at Princeton University. She obtained her PhD in Bar Ilan university in 2016, under the advisement of Prof. Yosi Keller. Her research focuses on analysis of noisy data provided by cryo-electron microscopy, as well as computationally efficient methods of dealing with big data and analysis of partial, biased data.

 

Current affiliation: The Program for Applied and Computational Mathematics at Princeton University.

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