Abstract
The incorporation of prior knowledge into a medical segmentation task allows to compensate for the issue of weak boundary definition and to be more in line with anatomical reality even though the data do not explicitly show these characteristics. This motivation underlies the proposed contribution which aims to provide a unified variational framework involving topological requirements in the training of convolutional neural networks through the design of a suitable penalty in the loss function. More precisely, these topological constraints are implicitly enforced by viewing the segmentation assignment as a registration task between the considered image and its associated ground truth under incompressibility condition, making them homeomorphic. The application falls within the scope of organ-at-risk segmentation in CT (Computed Tomography) images, in the context of radiotherapy planning.
This project was co-financed by the European Union with the European regional development fund (ERDF, 18P03390/18E01750/18P02733), by the Haute-Normandie Régional Council via the M2SINUM project and by the French Research National Agency ANR via AAP CE23 MEDISEG ANR project. The authors would like to thank the CRIANN (Centre Régional Informatique et d’Applications Numériques de Normandie, France) for providing computational resources.
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Lambert, Z., Le Guyader, C., Petitjean, C. (2023). On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy. In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_28
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