Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung
This work addresses lung cancer diagnosis by improving nodule analysis in medical imaging, but it is incremental as it modifies existing U-Net architectures.
The authors tackled the problem of lung nodule segmentation and texture classification from CT images, achieving the best nodule segmentation result on the LNDb challenge leaderboard.
In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular U-Net architecture family but modified for the joint nodule segmentation and its texture classification tasks and an ensemble-based model for the follow-up recommendation. This solution was evaluated within the LNDb medical imaging challenge and produced the best nodule segmentation result on the final leaderboard.