Making Sense Of What You See In Biomedical Images


It Recent years have seen dramatic improvements in imaging technologies that result in higher resolutions and faster acquisition times.  Images of single cells,  tissue and organs provide medical experts around the world with a myriad of information about their patients’ state of health at a given time. But how do they gain understanding of what they see in these biomedical images?

To make these large volumetric images reveal their true information potential, manual segmentation. whereby a digital image is divided into various segments to enable or facilitate analysis  is often required. Labels, such as for example “background” or “object”, are assigned to various structures of interest with different intervals inside the 3D volume. This is followed by an interpolation of the labels between the pre-segmented slices, where values at unknown points are estimated by using known data. In this process, the underlying image data is usually not taken into account, and the interpolation is therefore based exclusively on the segmented slices. Consequently, only a fraction of the real experimental information is utilized to derive the segmentation.

“Manual segmentation of large biomedical datasets of unknown composition is often very time-consuming and prone to errors. For analyzing three-dimensional image data, manual segmentation is still a very common approach. In fact, institutes employ armies of trained students just for this very task,” says Philipp Lösel from the research group “Data Mining and Uncertainty Quantification” (DMQ) at HITS, who developed Biomedisa.And this is where the Biomedical Image Segmentation App Biomedisa ( comes in, a free and easy-to-use open-source online platform especially developed for semi-automatic segmentation. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. This makes Biomedisa particularly valuable when little a priori knowledge is available. “Biomedisa can accelerate the segmentation process enormously, while at the same time providing more accurate results than the manual segmentation,” says Thomas van de Kamp (KIT), a biologist with painful experience in manual image segmentation who provided micro-CT data and evaluated Biomedisa during its development.

The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters. The one-button solution can be used for different 3D imaging modalities and various biomedical applications. “Our explicit aim,” summarizes Vincent Heuveline, director of Heidelberg University’s Computing Centre (URZ) and DMQ group leader at HITS, “was to create a widely-applicable and user-friendly tool to accelerate the segmentation of samples of unknown morphology while also improving the results.”

Source : Hits