2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
This work addresses the problem of accurate medical image segmentation for liver and tumor analysis, which is incremental as it builds on existing neural network methods with a cascaded design.
The paper tackled automatic segmentation of liver and tumors in CT images using a two-stage cascaded DenseNet approach, achieving a global dice score of 0.923 for liver and 0.625 for tumor segmentation, with a tumor burden RMSE of 0.044.
In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet). We independently train liver and tumor segmentation models and cascade them for a combined segmentation of the liver and its tumor. The first stage involves segmentation of liver and the second stage uses the first stage's segmentation results for localization of liver and henceforth tumor segmentations inside liver region. The liver model was trained on the down-sampled axial slices $(256 \times 256)$, whereas for the tumor model no down-sampling of slices was done, but instead it was trained on the CT axial slices windowed at three different Hounsfield (HU) levels. On the test set our model achieved a global dice score of 0.923 and 0.625 on liver and tumor respectively. The computed tumor burden had an rmse of 0.044.