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AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation

Abstract : Deep learning methods have gained increasing attention in addressing segmentation problems for medical images analysis despite the challenges inherited from the medical domain, such as limited data availability, lack of consistent textural or salient patterns, and high di-mensionality of the data. In this paper, we introduce a novel multi-network architecture that exploits domain knowledge to address those challenges. The proposed architecture consists of multiple deep neural networks that are trained after co-aligning multiple anatomies through multi-metric deformable registration. This multi-network architecture can be trained with fewer examples and leads to better performance, robustness and generalization through consensus. Comparable to human accuracy, highly promising results on the challenging task of interstitial lung disease segmentation demonstrate the potential of our approach.
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Submitted on : Monday, October 21, 2019 - 9:54:35 AM
Last modification on : Saturday, June 25, 2022 - 10:56:45 AM


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  • HAL Id : hal-01958236, version 2


Maria Vakalopoulou, Guillaume Chassagnon, Norbert Bus, Rafael Marini Silva, Evangelia I. Zacharaki, et al.. AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Sep 2018, Granada, Spain. ⟨hal-01958236v2⟩



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