AI-assisted annotation of large and multimodal imaging datasets
|AI-assisted annotation of large and multimodal imaging datasets
|Date of defense :
|Committee's member(s) :
|Number of pages :
[en] Computer vision
[en] Deep learning
|Engineering, computing & technology > Computer science
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|Université de Liège, Liège, Belgique
|Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
|Master thesis of the Faculté des Sciences appliquées
[en] The annotation of histological images through different stains is an important task for
diagnosis of diseases such as cancer, but it is also very time-consuming. Despite its
repetitive nature, doing such annotations for a same tissue in several stains still requires
specialized skills to be done. Nevertheless, the usage of computer vision and machine
learning techniques may be used to reduce the time needed to perform this task.
This thesis will try to reduce the time needed by developing methods allowing to use the
annotation from one tissue image and transfer it to its other modalities (i.e. images with
other stains). The annotations considered are freehand polygons, delimiting the area of
interest, and up to 25 stains per tissue are available in the dataset used.
To achieve such a transfer of annotation, several global feature-based and pixel-based registrations
of the whole images are compared. Afterwards, local registrations are performed
following the global ones to enhance the results. Those local adjustments are done either
using a second time the best techniques from the global registration or by performing a
local segmentation using a deep neural network. From those techniques, only the featurebased
methods lead to honorable results, with a second local adjustment achieving either
a much better or much worse registration. Even though the performances are not sufficient
to reliably perform the fully automatic transfer of annotations, the feature-based
methods may be used to give an estimate and reduce the interaction required from the
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