Human vs. machine: evaluation of fluorescence micrographs

Human vs. machine: evaluation of fluorescence micrographs

To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs. As a part of this, we focus on the question of how to measure the accuracy ...

Adaptive Local Thresholding for Fluorescence Cell Micrographs

An adaptive local thresholding method for microscope based high content analysis (HCA) is proposed. Cell micrographs of HCA contain detailed objects both in and out of focus, which cannot be correctly segmented by global thresholding methods. The proposed method utilizes adaptive local neighborhood size and double thresholding, and is able to produce segmentations that conform closely to percep...

Ranking vs. Regression in Machine Translation Evaluation

Automatic evaluation of machine translation (MT) systems is an important research topic for the advancement of MT technology. Most automatic evaluation methods proposed to date are score-based: they compute scores that represent translation quality, and MT systems are compared on the basis of these scores. We advocate an alternative perspective of automatic MT evaluation based on ranking. Inste...

Automatic Evaluation of Multi-Parameter Fluorescence Micrographs with a Neural Network Architecture

A neural network architecture for automatic segmentation of fluorescence micrographs

A system for the automatic segmentation of fluorescence micrographs is presented. In a first step positions of fluorescent cells are detected by a fast learning neural network, which acquires the visual knowledge from a set of training cell-image patches selected by the user. Guided by the detected cell positions the system extracts in the second step the contours of the cells. For contour extr...