logo EDITE Daniel Felipe GONZALEZ OBANDO
Identité
Daniel Felipe GONZALEZ OBANDO
État académique
Thèse en cours...
Sujet: From Digital to Computational Pathology for Biomarker Discovery
Direction de thèse:
Laboratoire:
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
oai:hal.archives-ouvertes.fr:hal-01578638
Quad-edge active contours for biomedical image segmentation
International audience
We investigate a novel, parallel implementation of active contours for image segmentation combining a multi-agent system with a quad-edge representation of the contour. The control points of the contour evolve independently from one another in a parallel fashion, handling contour deformation, and convergence, while the quad-edge representation simplifies contour manipulation and local re-sampling during its evolution. We illustrate this new approach on biological images, and compare results with a conventional active contour implementation, discussing its benefits and limitations. This preliminary work is made freely available as a plug-in for our open-source Icy platform, where it will be developed with future extensions.
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 2017 International Symposium on Biomedical Imaging (ISBI 2017) https://hal.archives-ouvertes.fr/hal-01578638 2017 International Symposium on Biomedical Imaging (ISBI 2017), Apr 2017, Melbourne, Australia. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp.1129-1132, 2017, <http://biomedicalimaging.org/2017/>. <10.1109/ISBI.2017.7950715> http://biomedicalimaging.org/2017/ARRAY(0x7f547126de90) 2017-04-18
oai:hal.archives-ouvertes.fr:hal-01578420
MULTI-STAINING REGISTRATION OF LARGE HISTOLOGY IMAGES
International audience
Quantifying T cells inside tumorous tissue can help identifying immune profiles in order to improve prognosis and possibly develop immunotherapy. However, to identify T cells and cancerous cells in two consecutive staining slides is challenging: the tissue preparation introduces the problem of alignment on large size images with poor visual common information. This work presents a framework for aligning whole slide images by extracting their common information and performing non-rigid registration based on B-splines to solve this problem. Experiments show good results with a mean error of 20.34 ± 12.20µm on our images even if some developments are still needed. This preliminary work is publicly available as part of our open-source Icy platform.
2017 International Symposium on Biomedical Imaging (ISBI 2017) https://hal.archives-ouvertes.fr/hal-01578420 2017 International Symposium on Biomedical Imaging (ISBI 2017), May 2017, Melbourne, Australia. 2017, <biomedicalimaging.org/2017/> biomedicalimaging.org/2017/ARRAY(0x7f547126dcc8) 2017-05-18