lung-tumor-segmentation

We propose a novel method for the volumetric segmentation of lung tumor volumes with encoder-decoder architectures which utilize multi-scale features from 2D convolutions as well as volumetric information using 3D convolutions.

We propose a pipeline for lung tumor detection and segmentation on the NSCLC Radiomics dataset. The pipeline utilized a hybrid-3d dilated convolutional neural network architecture for the segmentation task and won the IEEE VIP Cup 2018 challenge.