Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
<|reference_start|>Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation: The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE.<|reference_end|>
@article{sadikine2024semi-overcomplete,
    title={Semi-overcomplete convolutional auto-encoder embedding as shape priors
  for deep vessel segmentation},
    author={Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet,
  Dimitris Visvikis, Pierre-Henri Conze},
    journal={2022 IEEE ICIP, Bordeaux, France, 2022, pp. 586-590},
    year={2024},
    doi={10.1109/ICIP46576.2022.9897188},
    archivePrefix={arXiv},
    eprint={2409.13001},
    primaryClass={eess.IV cs.CV cs.LG}
}