1. Introduction

Molecular microscopy is, and will continue to be, one of the most important structural approaches in cell biological investigations. Currently, the technique requires the acquisition of very large numbers of high quality images from an electron microscope controlled by an experienced microscopist. This is a labor-intensive and slow methodology and it is clear that this situation must change if important biological problems are to be addressed in an expeditious manner. There is increasing interest in the field for fully automating the entire process of acquiring high quality transmission electron micrographs.

Typically, a microscopist identifies potential features of interest by visual inspection of a low magnification field of view. High magnification images of these identified features are then acquired using techniques which minimize the exposure of the specimen to electron beam damage. As a result, the high magnification image is never visually examined prior to acquisition. The quality of the high magnification image is assessed only after acquisition when the image can be analyzed and a decision made as to whether it warrants further processing. An experienced microscopist assimilates this quality assessment information and uses it to refine the choice of potentially relevant low magnification features. A simple brute force method in which the entire low magnification field of view is systematically examined is impractical because the field of view is very large and the scale change between the low and high magnification images is typically two orders of magnitude.

It should also be noted that the term automation is quite often used where "computer assisted" or "semi-automated" might be more appropriate (see for example [2]). While there are a number of excellent systems [1-3] which relieve the operator of many of the tedious tasks at the console, the operator needs to identify the regions of interest and provide input to the system during the data acquisition process. Our goal is to develop a system which can acquire several thousand images in a day with no input required from a human operator.

We have developed a system, called Leginon [4-5] to automatically acquire large numbers of acceptable quality images from specimens of negatively stained catalase, a biological protein which forms crystals. Acquiring good quality images of this specimen is often used as a test for students taking a course in electron microscopy and thus provides an excellent driver for the research methods that must be developed to solve the general problems of automated image acquisition. Furthermore, as catalase is an ordered crystalline structure, assessment of this order provides us with an objective measure of the quality of the automatically acquired images. In this paper we will describe the details of the system architecture which we have developed and analyze the performance of the system for the automated acquisition of a thousand images a day.

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