End-to-end Lung Nodule Detection in Computed Tomography

TL;DR

This paper introduces an end-to-end deep learning system for lung nodule detection directly from raw CT data, outperforming traditional methods that rely on reconstructed images.

Contribution

It presents a novel deep neural network pipeline combining raw data conversion and 3D detection, trained sequentially and fine-tuned end-to-end, enhancing detection performance in CT imaging.

Findings

Achieved comparable sensitivity to fully-sampled data detectors.

Demonstrated superior detection accuracy over image-based detectors.

Validated on LIDC-IDRI dataset with simulated projections.

Abstract

Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system op-erating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional neural network (3D-CNN) for the nodule detection. For efficient network training, the deep reconstruction network and the CNN detector was trained sequentially first, then followed by one epoch of end-to-end fine tuning. The method was evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI) with simulated forward projections. With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data. It also demonstrated superior detection performance compared to detectors trained on the reconstructed images. The proposed method is general and could be expanded to most detection tasks in medical imaging.