URDNet: A Unified Regression Network for GGO Detection in Lung CT Images

Abstract EN

We present a 3D deep neural network known as URDNet for detecting ground-glass opacity (GGO) nodules in 3D CT images.

Prior work on GGO detection repurposes classifiers on a large number of windows to perform detection or fine-tuning by box regression based on a previous window classification step.

Instead, we consider GGO detection as a multitarget regression problem to focus on the location of GGO.

Furthermore, to capture multiscale information, we introduce a backbone network which is a contracting-expanding structure similar to 2D U-net, but we inject the source CT inputs into each layer in the contracting pathway to prevent source information loss at different scales.

At last, we propose a two-stage training method for URDNet.

In the first stage, the backbone of the network for feature extraction is trained, and in the second, the overall URDNet is fine-tuned based on the previous pretrained weights.

By using this training method in conjunction with data augmentation and hard negative mining techniques, our URDNet can be effectively trained even on a small amount of annotated CT images.

We evaluate the proposed method on the LIDC-IDRI dataset.

It achieves the sensitivity of 90.8% with only 1 false positive per scan.

Experimental results show that our detection method achieves the superior detection performance over the state-of-the-art methods.

Due to its simplicity and effective, URDNet can be easier to apply to medical IoT systems for improving the efficiency of overall health systems.