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              <p>Biological image segmentation presents several challenges. Microscopes vary widely and are often optimized forspecific objects, leading to a great diversity of image types and structures to analyze. As a result, it is often necessary to train orfine-tune neural network weights for each specific problem. In this work, we propose a training method that allows model weights tobe optimized using very partial annotations, such as points, curve segments, or brush strokes applied to the interior of the objects tobe segmented. Our approach is strongly based on the eikonal equation, which defines the distance to object boundaries. Preliminaryresults are promising, and a Napari plugin has been developed to test the proposed solution.</p>
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              <p>La segmentation d'images biologiques présente de nombreux défis. Les microscopes utilisés sont variés et souvent optimisés en fonction des spécificités de l'objet à observer, ce qui entraîne une grande diversité de types d'images et d'objets à analyser. Ainsi, il est souvent nécessaire d'entraîner ou d'affiner les poids des réseaux de neurones pour chaque problème spécifique. Dans ce travail, nous proposons une méthode d'apprentissage permettant d'optimiser les poids d'un modèle à partir d'annotations très partielles, telles que des points, des segments de courbes ou des coups de pinceau appliqués sur l'intérieur des objets à segmenter. Notre approche repose sur l'équation eikonale, qui définit la distance aux frontières des objets. Les résultats préliminaires sont prometteurs, et une implémentation sous forme de plugin Napari permet de tester la solution proposée.</p>
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