Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
- URL: http://arxiv.org/abs/2409.13001v1
- Date: Thu, 19 Sep 2024 15:59:23 GMT
- Title: Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
- Authors: Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet, Dimitris Visvikis, Pierre-Henri Conze,
- Abstract summary: We present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder 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.
- Score: 0.9150396362246221
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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