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,...

Full description

Saved in:
Bibliographic Details
Main Authors: Sadikine, A., Badic, B., Tasu, J.-P., Noblet, V., Visvikis, D., Conze, P.-H.
Format: Conference Proceeding
Language:English
Subjects:
Blood vessels
Image analysis
Image segmentation
Liver
overcomplete representations
Pipelines
Shape
shape priors
Surgery
tubular structures
vascular segmentation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897188