Fitting Tree Model with CNN and Geodesics to Track Blood Vessels in 2D Medical Images and Application to Ultrasound Localization Microscopy Data

Théo Bertrand, Laurent Cohen

2024

Abstract

Segmentation of tubular structures in vascular imaging is a well studied task, although it is rare that we try to infuse knowledge of the tree-like structure of the regions to be detected. Our work focuses on detecting the important landmarks in the vascular network (via CNN performing both localization and classification of the points of interest) and representing vessels as the edges in some minimal distance tree graph. We leverage geodesic methods relevant to the detection of vessels and their geometry, making use of the space of positions and orientations so that 2D vessels can be accurately represented as trees. We build our model to carry tracking on Ultrasound Localization Microscopy (ULM) data, proposing to build a good cost function for tracking on this type of data. We also test our framework on synthetic and eye fundus data. Results show that the Orientation Score built from ULM data yields good geodesics for tracking blood vessels but scarcity of well annotated ULM data is an obstacle to the localization of vascular landmarks.

Paper Citation

in Harvard Style

Bertrand T. and Cohen L. (2024). Fitting Tree Model with CNN and Geodesics to Track Blood Vessels in 2D Medical Images and Application to Ultrasound Localization Microscopy Data. In Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE; ISBN 978-989-758-693-4, SciTePress, pages 44-51. DOI: 10.5220/0012723900003720

in Bibtex Style

@conference{improve24,
author={Théo Bertrand and Laurent Cohen},
title={Fitting Tree Model with CNN and Geodesics to Track Blood Vessels in 2D Medical Images and Application to Ultrasound Localization Microscopy Data},
booktitle={Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE},
year={2024},
pages={44-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012723900003720},
isbn={978-989-758-693-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE
TI - Fitting Tree Model with CNN and Geodesics to Track Blood Vessels in 2D Medical Images and Application to Ultrasound Localization Microscopy Data
SN - 978-989-758-693-4
AU - Bertrand T.
AU - Cohen L.
PY - 2024
SP - 44
EP - 51
DO - 10.5220/0012723900003720
PB - SciTePress