BibTex format
@unpublished{Batten:2023,
author = {Batten, J and Sinclair, M and Glocker, B and Schaap, M},
title = {Image To Tree with Recursive Prompting},
url = {http://arxiv.org/abs/2301.00447v1},
year = {2023}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - UNPB
AB - Extracting complex structures from grid-based data is a common key step inautomated medical image analysis. The conventional solution to recoveringtree-structured geometries typically involves computing the minimal cost paththrough intermediate representations derived from segmentation masks. However,this methodology has significant limitations in the context of projectiveimaging of tree-structured 3D anatomical data such as coronary arteries, sincethere are often overlapping branches in the 2D projection. In this work, wepropose a novel approach to predicting tree connectivity structure whichreformulates the task as an optimization problem over individual steps of arecursive process. We design and train a two-stage model which leverages theUNet and Transformer architectures and introduces an image-based promptingtechnique. Our proposed method achieves compelling results on a pair ofsynthetic datasets, and outperforms a shortest-path baseline.
AU - Batten,J
AU - Sinclair,M
AU - Glocker,B
AU - Schaap,M
PY - 2023///
TI - Image To Tree with Recursive Prompting
UR - http://arxiv.org/abs/2301.00447v1
ER -