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Quantitative analysis of medical images: finding relevant regions-of-interest for medical decision support

Defense Thèse de doctorat : Univ. Genève, 2017 - Sc. 5106 - 2017/05/18
Abstract In the past decades the number of medical images inspected daily in health centers, as well as the complexity of imaging parameters have increased tremendously. An efficient quantitative analysis could improve health care by enabling a more objective interpretation of these imaging studies. The main goal of this thesis was to propose and evaluate novel methods that detect and quantify regions-of-interest (ROIs) in medical images. Challenges in medical image annotation and medical case-based retrieval were organized within a research group (VISCERAL) and are reviewed as a scientific contribution of this work. Moreover, multimodal (using both text and visual data) medical case-based retrieval systems are proposed both for radiology and digital pathology data, tackling the navigation of large-scale hospital repositories. By segmenting anatomical structures in full patient scans and measuring visual features in preselected regions, medical professionals can then prioritize their attention to the more significant structures in the images.
Keywords Evaluation frameworkOrgan segmentationRegion-of-interest detectionMedical case-based retrievalWhole-slide image classificationBiomedical texture analysisDeep learning
URN: urn:nbn:ch:unige-962970
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Other version: http://www.visceral.eu
Research groups MedGIFT
Computer Vision and Multimedia Laboratory
Groupe Geissbuhler Antoine (informatique médicale) (222)
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JIMENEZ DEL TORO, Oscar. Quantitative analysis of medical images: finding relevant regions-of-interest for medical decision support. Université de Genève. Thèse, 2017. https://archive-ouverte.unige.ch/unige:96297

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Deposited on : 2017-08-28

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