Dr. Ramin Norousi

Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are detected on and boxed out from each micrograph using fully- or semi-automated approaches. However, the obtained particles still require laborious manual post-picking classification, which is one major bottleneck for single particle analysis of large datasets. We introduce MAPPOS, a supervised post-picking strategy for the classification of boxed particle images, as additional strategy adding to the already efficient automated particle picking routines. MAPPOS employs machine learning techniques to train a robust classifier from a small number of characteristic image features. In order to accurately quantify the performance of MAPPOS we used simulated particle and non-particle images. In addition, we verified our method by applying it to an experimental cryo-EM dataset and comparing the results to the manual classification of the same dataset. Comparisons between MAPPOS and manual post-picking classification by several human experts demonstrated that merely a few hundred sample images are sufficient for MAPPOS to classify an entire dataset with a human-like performance. MAPPOS was shown to greatly accelerate the throughput of large datasets by reducing the manual workload by orders of magnitude while maintaining a reliable identification of non-particle images.

Keywords: Electron microscopy - Particle picking - Machine learning - Classification ensemble - 3D cryo-EM density map

For more information, take a look on my paper at Journal of Structural Biology

MAPPOS: Machine learning Algorithm for Particle POSt-picking

Dr. Ramin Norousi (2013)

MAPPOS is a software package for classification of windowed micrograph images which are output by common picking software (e.g. Signature) in the 3D cryo electron microscopy (3DEM). It replaces the laborious manual post processing step, which is a bottleneck for next generation of electron microscopy. It is implemented in Matlab and runs on any platform for which is Matlab available.

Method:

MAPPOS is devided into learning phase and predicition phhase. The toolbox is equipped with several model classes for out-of-box usage. Instead of learning one classifier, it uses an ensemble method, in which the classification is the majority vote of a number of individual classifiers. It uses decision trees as the basic learning method. The fact that there is no single explicit parameter that needs to be tuned by the user greatly contibutes to the robustness of MAPPOS and its user-friendliness.

For more information, you can download my paper directly from my website My Paper about MAPPOS

Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis

Dr. Ramin Norousi, Volker J. Schmid (2016)

Since manual object detection is very inaccurate and time consuming, some automatic object detection tools have been developed in recent years. At the moment, there is no image analysis software available which provides an automatic, objective assessment of 3D foci which is generally applicable. Complications arise from discrete foci which are very close or even come in contact to other foci, moreover they are of variable sizes and show variable signal-to-noise, and must be analyzed fully in 3D. Therefore we introduce the 3D-OSCOS (3D-Object Segmentation and Colocalization Analysis based on Spatial statistics) algorithm which is implemented as a user-friendly toolbox for interactive detection of 3D objects and visualization of labeled images.

Keywords: 3D Object Detection - Fluorescent - Spatial Statistics

For more information, take a look on my paper at Cornell University Library