Digital pathology is a new, rapidly expanding field in medical imaging. In digital pathology whole-slide scanners are used to digitize glass slides containing tissue specimens at high resolution (up to 160 nm per pixel), producing giga-pixel whole-slide images.
One of the principal tasks of pathologists is to detect cancer in histopathology images. Staining techniques are used to visualize parts of the tissue that are relevant for the diagnosis. As an example, hematoxilyn and eosin (H&E) staining is used to color cell nuclei in blue and connective tissue in pink. Immunohistochemistry (IHC) is a more advanced staining technique, used to highlight specific cells which exhibit, for instance, a function in the tumor growing mechanism. In all cases, the goal is to detect and characterize tumors.
Digitized histopathology images contain a huge amount of information, which represents a perfect source of data to develop deep learning algorithms for automatic image analysis. This is clearly demonstrated by the fact that in the Camelyon16 challenge, which we recently organized, the ten best scoring algorithms were all based on deep learning. In this challenge, we assessed the state of the art technology for solving the task of metastasis detection in sentinel lymph nodes.
Large databases of digital pathology images are available in our group, consisting of images of breast, colorectal, head and neck cancer, among others. Software libraries for handling gigapixel whole-slide images are available, as well as a workstation developed in house to visualize and annotate whole-slide images. Several projects are available for master students to work on deep learning for automatic analysis of digital pathology images (see Tasks). Deep learning algorithms will be developed based on the libraries Theano/Lasagne and TensorFlow.
The work will be executed in a high level environment for medical image analysis, at the Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center. DIAG is a leading research group in computer-aided detection and diagnosis (CAD).
Here are possible tasks suitable for your research project: