How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?

TL;DR

This study investigates optimal slice selection strategies for annotating cross-sectional medical images to train deep learning segmentation models efficiently, revealing that annotating fewer slices per volume across more cases is generally best, and that active learning offers limited benefits.

Contribution

It systematically evaluates slice selection techniques and annotation strategies, providing practical guidelines for maximizing segmentation model performance with limited annotation resources.

Findings

Annotating fewer slices per volume and more volumes improves performance.

Unsupervised active learning does not outperform random or fixed interval selection.

Mask interpolation rarely improves model accuracy, except in specific 3D configurations.

Abstract

Automated segmentation of medical images heavily relies on the availability of precise manual annotations. However, generating these annotations is often time-consuming, expensive, and sometimes requires specialized expertise (especially for cross-sectional medical images). Therefore, it is essential to optimize the use of annotation resources to ensure efficiency and effectiveness. In this paper, we systematically address the question: "in a non-interactive annotation pipeline, how should slices from cross-sectional medical images be selected for annotation to maximize the performance of the resulting deep learning segmentation models?" We conducted experiments on 4 medical imaging segmentation tasks with varying annotation budgets, numbers of annotated cases, numbers of annotated slices per volume, slice selection techniques, and mask interpolations. We found that: 1) It is almost always preferable to annotate fewer slices per volume and more volumes given an annotation budget. 2) Selecting slices for annotation by unsupervised active learning (UAL) is not superior to selecting slices randomly or at fixed intervals, provided that each volume is allocated the same number of annotated slices. 3) Interpolating masks between annotated slices rarely enhances model performance, with exceptions of some specific configuration for 3D models.