Automated Pulmonary Nodule Detection Using 3D Deep Convolutional Neural Networks




June 25th, 2017


Daniel Kim
Xiaohui Xie




Lung cancer has been the leading cause of all cancer-related deaths, causing 1.3 millions death annually. Detecting pulmonary nodules early is critical for a good prognosis of the disease, and low-dose computed tomography (CT) scans are widely used and very effective for this purpose. However, manually screening CT images is time-consuming for radiologists who are increasingly overwhelmed with data. Advanced computer-aided diagnosis systems (CADs) have the potential to expedite this process but the task is complicated by the variation in nodule size (from 3 to 50 mm), shape, density, and anatomical context, as well as the abundance of tissues that resemble the appearance of nodules (e.g., blood vessels, chest wall).


Our proposed method for nodule detection roughly follows two stages:


Candidate screening using a 3D Faster RCNN


Subsequent false positive reduction using 3D DCNN classifiers

The purpose of the Faster R-CNN in (1) is to identify nodule candidates while preserving high sensitivity, whereas the classifiers in (2) finely discriminate between true nodules and false positives. We find optimal results when models from both stages are ensembled for final predictions. Rather than using one stage in which we heavily retrain the Faster R-CNN with hard examples, we believe the two-stage framework provides more flexibility in adjusting the trade-off between sensitivity and specificity.


Our fully three-dimensional framework of automatic pulmonary nodule detection. It consists of a U-Net-like 3D Faster R-CNN, trained with online hard negative mining, and a 3D classifier for false positive reduction. We introduce a consensus ensembling method to integrate both models for predictions.

We validate our method in the 2017 TianChi Healthcare AI Competition, achieving superior performance (0.815 CPM). We believe our model is a powerful clinical tool that harnesses state-of-the-art architectures in a way that captures the spatial nature of CT data.

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