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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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AI-assisted annotation of large and multimodal imaging datasets

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Bernard, Simon ULiège
Promotor(s) : Geurts, Pierre ULiège ; Marée, Raphaël ULiège
Date of defense : 6-Sep-2021/7-Sep-2021 • Permalink : http://hdl.handle.net/2268.2/13291
Details
Title : AI-assisted annotation of large and multimodal imaging datasets
Author : Bernard, Simon ULiège
Date of defense  : 6-Sep-2021/7-Sep-2021
Advisor(s) : Geurts, Pierre ULiège
Marée, Raphaël ULiège
Committee's member(s) : Wehenkel, Louis ULiège
Drion, Guillaume ULiège
Language : English
Number of pages : 97
Keywords : [en] Annotation
[en] AI
[en] Computer vision
[en] Histopathology
[en] Registration
[en] Deep learning
[en] Image
Discipline(s) : Engineering, computing & technology > Computer science
Complementary URL : https://github.com/Asefy/Annotation-multimodal-biomedical
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[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
annotator.

Author

  • Bernard, Simon ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Wehenkel, Louis ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Drion, Guillaume ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
  • Total number of views 91
  • Total number of downloads 116










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