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23 January 2025 |
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Article overview
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Image To Tree with Recursive Prompting | | James Batten
; Matthew Sinclair
; Ben Glocker
; Michiel Schaap
; | | Date: |
1 Jan 2023 | | Abstract: | Extracting complex structures from grid-based data is a common key step in
automated medical image analysis. The conventional solution to recovering
tree-structured geometries typically involves computing the minimal cost path
through intermediate representations derived from segmentation masks. However,
this methodology has significant limitations in the context of projective
imaging of tree-structured 3D anatomical data such as coronary arteries, since
there are often overlapping branches in the 2D projection. In this work, we
propose a novel approach to predicting tree connectivity structure which
reformulates the task as an optimization problem over individual steps of a
recursive process. We design and train a two-stage model which leverages the
UNet and Transformer architectures and introduces an image-based prompting
technique. Our proposed method achieves compelling results on a pair of
synthetic datasets, and outperforms a shortest-path baseline. | | Source: | arXiv, 2301.00447 | | Services: | Forum | Review | PDF | Favorites | |
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