Unsupervised Shape Normality Metric for Severity Quantification

TL;DR

This paper introduces an unsupervised shape normality metric (SNM) that quantifies anatomical shape abnormality using only normal samples, aiding in pathology detection without requiring pathological data.

Contribution

The novel SNM method learns from normal shapes alone and models their distribution to objectively measure shape abnormality, reducing bias and data scarcity issues.

Findings

SNM effectively detects anatomical pathologies across multiple datasets.

SNM outperforms traditional supervised methods in abnormality quantification.

The approach is applicable to various anatomical structures.

Abstract

This work describes an unsupervised method to objectively quantify the abnormality of general anatomical shapes. The severity of an anatomical deformity often serves as a determinant in the clinical management of patients. However, experiential bias and distinctive random residuals among specialist individuals bring variability in diagnosis and patient management decisions, irrespective of the objective deformity degree. Therefore, supervised methods are prone to be misled given insufficient labeling of pathological samples that inevitably preserve human bias and inconsistency. Furthermore, subjects demonstrating a specific pathology are naturally rare relative to the normal population. To avoid relying on sufficient pathological samples by fully utilizing the power of normal samples, we propose the shape normality metric (SNM), which requires learning only from normal samples and zero knowledge about the pathology. We represent shapes by landmarks automatically inferred from the data and model the normal group by a multivariate Gaussian distribution. Extensive experiments on different anatomical datasets, including skulls, femurs, scapulae, and humeri, demonstrate that SNM can provide an effective normality measurement, which can significantly detect and indicate pathology. Therefore, SNM offers promising value in a variety of clinical applications.