Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction

TL;DR

This paper introduces volume uniformizing methods for 3D CT scan processing with CNNs, improving tuberculosis prediction accuracy by leveraging full 3D information while managing computational constraints.

Contribution

It proposes novel volume uniformizing techniques that enhance 3D CNN performance on volumetric medical data, outperforming slice-based approaches.

Findings

Achieved 73% AUC on TB prediction benchmark

Reported 67.5% accuracy, ranking 5th overall

Demonstrated effectiveness of uniformizing methods through ablation studies

Abstract

A common approach to medical image analysis on volumetric data uses deep 2D convolutional neural networks (CNNs). This is largely attributed to the challenges imposed by the nature of the 3D data: variable volume size, GPU exhaustion during optimization. However, dealing with the individual slices independently in 2D CNNs deliberately discards the depth information which results in poor performance for the intended task. Therefore, it is important to develop methods that not only overcome the heavy memory and computation requirements but also leverage the 3D information. To this end, we evaluate a set of volume uniformizing methods to address the aforementioned issues. The first method involves sampling information evenly from a subset of the volume. Another method exploits the full geometry of the 3D volume by interpolating over the z-axis. We demonstrate performance improvements using controlled ablation studies as well as put this approach to the test on the ImageCLEF Tuberculosis Severity Assessment 2019 benchmark. We report 73% area under curve (AUC) and binary classification accuracy (ACC) of 67.5% on the test set beating all methods which leveraged only image information (without using clinical meta-data) achieving 5-th position overall. All codes and models are made available at https://github.com/hasibzunair/uniformizing-3D.