Towards unsupervised segmentation in high-resolution medical nano-imaging
Dietlmeier, JuliaORCID: 0000-0001-9980-0910, Ghita, Ovidiu and Whelan, Paul F.ORCID: 0000-0002-2029-1576
(2011)
Towards unsupervised segmentation in high-resolution medical nano-imaging.
In: Bioengineering...in Ireland 17, 17th annual conference of the bioengineering section of the royal academy of medicine in Ireland, 28-29 Jan 2011, Galway.
Recent advances in cellular and subcellular microscopy demonstrated its potential towards unraveling the mechanisms of various diseases at the molecular level. From a computer vision perspective nano-imaging is an inherently complex environment as can for example be seen from Fig.1(a,c). For the image analysis of intracellular organisms in high-resolution microscopy, new techniques which are capable of handling high-throughput data in a single pass and real time are of special interest. The additional emphasis is put therein on automated solutions which can provide the objective quantitative information in a reasonable time frame. The state-of-the-art is dominated by manual data annotation[1]and the early attempts to automate the segmentation are based on statistical machine-learning techniques[4].