Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

TL;DR

This paper introduces reverse classification accuracy (RCA), a novel framework that predicts segmentation performance without ground truth by training a reverse classifier on predicted segmentations and evaluating it on reference images.

Contribution

The paper proposes RCA as a new method to estimate segmentation quality in the absence of ground truth, validated across multiple datasets and classifiers.

Findings

RCA can effectively predict segmentation quality without ground truth.

The approach generalizes across different segmentation methods and classifiers.

RCA is suitable for clinical routine and large-scale image analysis.

Abstract

When integrating computational tools such as automatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross-validation because validation data is often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as part of large-scale image analysis studies.