Image To Tree with Recursive Prompting

• URL: http://arxiv.org/abs/2301.00447v1
• Date: Sun, 1 Jan 2023 17:35:24 GMT
• Title: Image To Tree with Recursive Prompting
• Authors: James Batten, Matthew Sinclair, Ben Glocker, Michiel Schaap
• Abstract summary: We propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.
• Score: 16.56278942191951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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