BioImage Informatics: The challenge of knowledge extraction from biological images

Abstract

Bioimage Informatics is a rapidly growing research field that is giving fundamental contributions to research in biology and biomedicine aiming at facilitating the extraction of quantitative information from images. Great advances in biological tissue labeling and microscopic imaging are radically changing how biologists visualize and study the molecular and cellular structures. These devices nowadays produce terabytesized multi-dimensional images: how to automatically and efficiently extract objective knowledge from such images has become a major challenge. In this manuscript we analyze the state-of-theart of Bioimage Informatics, with a special focus on neuroscience. We show that there are increasing efforts to deliver methods and software tools providing functionalities for visualization, representation, management and analysis of 3D multichannel images. Nevertheless, most of them have been applied on datasets with size of MVoxel or few GVoxel, where the variations in contrast, illumination, as well as object shape and dimensions are limited. The huge dimensions of new 3D image stacks therefore ask for fully automated processing methods, whose parameters should be dynamically adapted to different regions in the volume. In this respect, this manuscript deepens in a recent contribution that digitally charts the Purkinje cells of whole mouse cerebellum, corresponding to an image dataset of 120 GVoxels.

Frontiers in intravital multiphoton microscopy of cancer

“…In neuroscience and developmental biology, the field of computer science is contributing new image processing and analysis workflows, advancing large‐scale brain mapping and analysis of tissue morphodynamics . Implementing similar unbiased strategies for IV‐MPM studies of cancer will allow us to accelerate the pace of discoveries.…”

“…41,42 This work also indicated that tumor cell behavior is a result of complex interactions among cues present in the tumor microenvironment and that machine learning algorithms have the ability to grasp this complexity. 18,43 In neuroscience and developmental biology, the field of computer science is contributing new image processing and analysis workflows, advancing large-scale brain mapping [44][45][46] and analysis of tissue morphodynamics. [47][48][49] Implementing similar unbiased strategies for IV-MPM studies of cancer will allow us to accelerate the pace of discoveries.…”