  Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of volumetric images. In this
work, we show that a multi-class 3D FCN trained on manually labeled CT scans of
several anatomical structures (ranging from the large organs to thin vessels)
can achieve competitive segmentation results, while avoiding the need for
handcrafting features or training class-specific models.
  To this end, we propose a two-stage, coarse-to-fine approach that will first
use a 3D FCN to roughly define a candidate region, which will then be used as
input to a second 3D FCN. This reduces the number of voxels the second FCN has
to classify to ~10% and allows it to focus on more detailed segmentation of the
organs and vessels.
  We utilize training and validation sets consisting of 331 clinical CT images
and test our models on a completely unseen data collection acquired at a
different hospital that includes 150 CT scans, targeting three anatomical
organs (liver, spleen, and pancreas). In challenging organs such as the
pancreas, our cascaded approach improves the mean Dice score from 68.5 to
82.2%, achieving the highest reported average score on this dataset. We compare
with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and
achieve a significantly higher performance in small organs and vessels.
Furthermore, we explore fine-tuning our models to different datasets.
  Our experiments illustrate the promise and robustness of current 3D FCN based
semantic segmentation of medical images, achieving state-of-the-art results.
Our code and trained models are available for download:
https://github.com/holgerroth/3Dunet_abdomen_cascade.
