Stereology

A Brief History Of Contemporary Stereology

Stereology literally translates in the Greek as, “study regarding objects in 3-D.” The Three-D analysis of objects dates to ancient Egypt and the introduction of Euclidean geometry. Stereology, however, formally started like a scientific discipline until under fifty years ago in a meeting of diverse researchers from fields of biology, geology, engineering, and materials sciences in 1961. A biologist, Professor Hendes Elias, had the concept to arrange this meeting in a resort known as the Feldberg within the Black Forest of Germany for the advantage of scientists in a number of disciplines who’d one factor in keeping: These were battling using the quantitative analysis of three-D images according to the look of them on 2-D sections. Only at that meeting, Prof. Elias recommended stereology like a helpful term to explain their discussions.

Soon after the very first stereology meeting around the Feldberg, Prof. Elias sent a little announcement around the proceedings towards the journal Science. Soon after that, he received a powerful response from researchers in academia, government departments, and industry at institutions all over the world. They contacted Prof. Elias for details about the following stereology meeting. What Elias suspected have been right — scientists across broad disciplines needed now methods for the analyses of three-D objects according to the look of them on 2-D sections.

The Worldwide Society For Stereology

The year after the Worldwide Society For Stereology (ISS) started using the first Congress from the Worldwide society for Stereology (ISS). Only at that congress, Prof. Hendes Elias was elected the founding president (Table 1).

The Very First Decade Of Stereology (1961-1971)

Because of recent technologies in microscopy, biologists within the 1960s could view tissues, cells, bloodstream vessels along with other objects in tissue with greater clearness and specificity than in the past. These developments incorporated the supply of affordable, high-resolution optics for light microscopy refinements in electron microscopy instruments and techniques for all of examples and, immune-based visualization of specific proteins in biological tissue (immunocytochemistry). Having the ability to see more objects in depth than in the past, they started to inquire about the apparent question: Just how much can there be?

To reply to this, biologists centered on an easy goal: To acquire reliable 3-D details about biological objects according to their 2-D appearance. For tips on how to proceed, they switched toward the aim mathematic-based methods emerging from the concept of stereology.

At ISS congresses held almost every other year, stereologists from many disciplines started to provide research and discuss their theories about how better to solve their common problems. Biologists attending these conferences learned that their stereology colleagues in various fields acquired practical approaches that might be of immediate use within their research, such as the following:

In 1637, Bonaventura Cavalieri, students of Galileo Galilei in Florence throughout the high Italian Renaissance, demonstrated the mean amount of a population of non-classically formed objects might be believed precisely from the sum of the areas around the cut surfaces from the objects (right). The Cavalieri Principle offers the foundation for the volume estimation of biological structures using their areas on tissue sections.

In 1777, Count George Leclerc Buffon presented the Needle Problem towards the Royal Academy of Sciences in Paris, France. The Needle Problem increases the probability theory for current methods to estimate the top area and period of biological objects within an impartial (accurate) manner.

In 1847, in france they mining engineer and geologist, Auguste Delesse, shown the expected value for the level of an item varies in directly proportion towards the observed area on the random section cut with the object. The Delesse Principle offers the grounds for accurate and efficient estimation of object and regions volumes by point counting.

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