Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

TL;DR

This paper introduces a novel 3D context modeling approach with supervised pre-training for improved universal lesion detection in CT slices, achieving state-of-the-art results and faster convergence.

Contribution

It proposes a Modified Pseudo-3D FPN with a new pre-training method using 2D natural images, enhancing 3D medical image analysis performance.

Findings

Achieves 3.48% higher sensitivity on DeepLesion dataset

Surpasses baseline by up to 6.06% in MAP@0.5

Pre-trained weights improve other 3D medical tasks

Abstract

Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D context modeling is of great significance for developing automated lesion detection algorithms. In this work, we propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) that leverages depthwise separable convolutional filters and a group transform module (GTM) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. To facilitate faster convergence, a novel 3D network pre-training method is derived using solely large-scale 2D object detection dataset in the natural image domain. We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3.48% absolute improvement in the sensitivity of FPs@0.5), significantly surpassing the baseline method by up to 6.06% (in MAP@0.5) which adopts 2D convolution for 3D context modeling. Moreover, the proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.