Toward Using Machine Learning as a Shape Quality Metric for Liver Point Cloud Generation

TL;DR

This paper explores using machine learning models, including PointNet, as interpretable, lightweight quality metrics for evaluating generated liver shapes in 3D medical modeling, addressing the challenge of individual shape assessment without ground truth.

Contribution

It introduces a supervised ML approach using handcrafted features and PointNet to classify liver shapes as good or bad, providing an interpretable and efficient quality assessment method.

Findings

ML classifiers offer interpretable feedback.

Models provide complementary insights to expert review.

Proposed approach supports transparent evaluation in medical shape generation.

Abstract

While 3D medical shape generative models such as diffusion models have shown promise in synthesizing diverse and anatomically plausible structures, the absence of ground truth makes quality evaluation challenging. Existing evaluation metrics commonly measure distributional distances between training and generated sets, while the medical field requires assessing quality at the individual level for each generated shape, which demands labor-intensive expert review. In this paper, we investigate the use of classical machine learning (ML) methods and PointNet as an alternative, interpretable approach for assessing the quality of generated liver shapes. We sample point clouds from the surfaces of the generated liver shapes, extract handcrafted geometric features, and train a group of supervised ML and PointNet models to classify liver shapes as good or bad. These trained models are then used as proxy discriminators to assess the quality of synthetic liver shapes produced by generative models. Our results show that ML-based shape classifiers provide not only interpretable feedback but also complementary insights compared to expert evaluation. This suggests that ML classifiers can serve as lightweight, task-relevant quality metrics in 3D organ shape generation, supporting more transparent and clinically aligned evaluation protocols in medical shape modeling.