# Learning a sparse database for patch-based medical image segmentation

## TL;DR

This paper presents a novel energy minimization approach to optimize a sparse database for patch-based medical image segmentation, significantly reducing database size while maintaining accuracy and improving clinical specificity.

## Contribution

It introduces a new functional for database optimization that incorporates fidelity, sparseness, and robustness, formulated as an energy minimization problem solved with standard numerical tools.

## Key findings

- Database size reduced by 96% without losing segmentation accuracy.
- Optimized database improved specificity of fractional flow reserve measurements.
- Method outperforms existing prototype selection approaches.

## Abstract

We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomography angiography (CCTA) data. The proposed functional consists of fidelity, sparseness and robustness to small-variations terms and their associated weights. Existing work address database optimization by prototype selection aiming to optimize the database by either adding or removing prototypes according to a set of predefined rules. In contrast, we formulate the database optimization task as an energy minimization problem that can be solved using standard numerical tools. We apply the proposed database optimization functional to the task of optimizing a database for patch-base coronary lumen segmentation. Our experiments using the publicly available MICCAI 2012 coronary lumen segmentation challenge data show that optimizing the database using the proposed approach reduced database size by 96% while maintaining the same level of lumen segmentation accuracy. Moreover, we show that the optimized database yields an improved specificity of CCTA based fractional flow reserve (0.73 vs 0.7 for all lesions and 0.68 vs 0.65 for obstructive lesions) using a training set of 132 (76 obstructive) coronary lesions with invasively measured FFR as the reference.