In recent years, bioimage analysis has emerged as a crossroad between diverse disciplines, such as theories of optics, computational image processing, signal processing, biophysics, biochemistry and molecular biology. This new field has rapidly garnered interest due to the diverse expertises and profiles of its community. Nonetheless, it is plagued, since its inception, with the feeling that it is a complex discipline. This wrong impression is directly linked to its interdisciplinary, each discipline having developed its own vocabulary causing confusions within the bioimage analysis community. By not sharing the discipline-specific terms and concepts, our community has lacked common denominators and definitions. Ideally, all the scientists involved in bioimage analysis should share and use the same definitions and concepts, but at this very early stage of an emerging field, we are still in the transition to make agreements in various directions. 
Building bridges between these disciplines is essential to alleviate these communication discrepencies and strengthen each discipline potentials with the others. Our effort has led to the development of BISE, a web-based infrastructure designed to open doors to different types of experts to integrate various types of knowledge. Particularly we want to discreetly link the vocabularies of the different disciplines, in a transparent way for the users.  Let's take an example: a life scientist desiring checking  if a specific regulatory protein is involded within the distance measured between a centromere and a kinetochore. The usual process would be to filter the image with a Laplacian kernel to enhance the dotty signals, facilitating the detection of both structures. Having a marginal knowledge in computational science, the life scientist will struggle to find this simple solution. On the other hand, a computer scientist will understand the mathematical concept and output of a Laplacian kernel, but will fail to link it to some key biological problem without instructions provided by the life scientist. The purpose of BISE is to help both scientists by transparently linking Centromere to Laplacian filetring, resulting in the possibility for the researchers to dive into different knowledge and skills without actually needing to know or learn these foreign vocabularies.