Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation
- URL: http://arxiv.org/abs/2509.08580v1
- Date: Wed, 10 Sep 2025 13:30:39 GMT
- Title: Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation
- Authors: Mathilde Monvoisin, Louise Piecuch, Blanche Texier, Cédric Hémon, Anaïs Barateau, Jérémie Huet, Antoine Nordez, Anne-Sophie Boureau, Jean-Claude Nunes, Diana Mateus,
- Abstract summary: This paper introduces an implicit shape prior to segment volumes from sparse slice manual annotations generalized to the multi-organ case, along with a simple framework for automatically selecting the most informative slices to guide and minimize the next interactions.<n>Experiments show the method's effectiveness on two medical use cases: assisted segmentation in the context of at risks organs for brain cancer patients, and acceleration of the creation of a new database with unseen muscle shapes for patients with sarcopenia.
- Score: 1.1325026208770568
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The objective of this paper is to significantly reduce the manual workload required from medical professionals in complex 3D segmentation tasks that cannot be yet fully automated. For instance, in radiotherapy planning, organs at risk must be accurately identified in computed tomography (CT) or magnetic resonance imaging (MRI) scans to ensure they are spared from harmful radiation. Similarly, diagnosing age-related degenerative diseases such as sarcopenia, which involve progressive muscle volume loss and strength, is commonly based on muscular mass measurements often obtained from manual segmentation of medical volumes. To alleviate the manual-segmentation burden, this paper introduces an implicit shape prior to segment volumes from sparse slice manual annotations generalized to the multi-organ case, along with a simple framework for automatically selecting the most informative slices to guide and minimize the next interactions. The experimental validation shows the method's effectiveness on two medical use cases: assisted segmentation in the context of at risks organs for brain cancer patients, and acceleration of the creation of a new database with unseen muscle shapes for patients with sarcopenia.
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