Lung nodule segmentation with convolutional neural network trained by simple diameter information

Chang-Mo Nam, Jihang Kim, Kyong Joon Lee

Abstract: Lung nodule segmentation can help radiologists' analysis of nodule risk. Recent deep learning based approaches have shown promising results in the segmentation task. However, a 3D segmentation map necessary for training the algorithms requires an expensive effort from expert radiologists. We propose a new method to train the deep neural network, only utilizing diameter information for each nodule. We validate our model with the LUNA16 dataset, showing competitive results compared to the previous state-of-the-art methods in various evaluation metrics. Our experiments also provide plausible qualitative results comparable to the ground truth segmentation.

Keywords: lung nodule, 3D segmentation, deep learning, simple diameter information

Author Affiliation: Seoul National University Bundang Hospital, Republic of Korea