On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening

Abstract

We address the automatic contraband material detection problem within volumetric 3D Computed Tomography (CT) data for baggage security screening. Distinct from the prohibited item detection using object detection techniques, contraband material detection is usually formulated as a segmentation problem due to the variations of their potential appearances and shapes. Previous studies have employed either morphological operation based traditional methods or 3D Convolutional Neural Networks (CNN) for 3D segmentation towards target material detection within volumetric 3D CT baggage security screening imagery. In this work, we investigate the effectiveness of 2D semantic segmentation techniques in this 3D CT segmentation problem. Specifically, we extract 2D slices from three planes of the 3D CT volumes and train a 2D segmentation model which is subsequently used to predict segmentation results for all the slices from a given test CT volume. Moreover, we also evaluate how the performance is affected when using a reduced number of annotated slices for training. As a result, it is demonstrated reasonable performance can be achieved with very limited annotated slices (1–2) per CT volume during training. Finally, we propose a semi-supervised learning framework for 3D CT segmentation. Using only 1/128 of the total number of annotated slices, our framework can achieve comparable performance with full supervision.