Title: Hierarchical 3D fully convolutional networks for multi-organ segmentation,
Abstract: Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of full volumetric images. In
this work, we show that a multi-class 3D FCN trained on manually labeled CT
scans of seven abdominal structures (artery, vein, liver, spleen, stomach,
gallbladder, and pancreas) can achieve competitive segmentation results, while
avoiding the need for handcrafting features or training organ-specific models.
To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN
model to roughly delineate the organs of interest in the first stage (seeing
$\sim$40% of the voxels within a simple, automatically generated binary mask of
the patient's body). We then use these predictions of the first-stage FCN to
define a candidate region that will be used to train a second FCN. This step
reduces the number of voxels the FCN has to classify to $\sim$10% while
maintaining a recall high of $>$99%. This second-stage FCN can now focus on
more detailed segmentation of the organs. We respectively utilize training and
validation sets consisting of 281 and 50 clinical CT images. Our hierarchical
approach provides an improved Dice score of 7.5 percentage points per organ on
average in our validation set. We furthermore test our models on a completely
unseen data collection acquired at a different hospital that includes 150 CT
scans with three anatomical labels (liver, spleen, and pancreas). In such
challenging organs as the pancreas, our hierarchical approach improves the mean
Dice score from 68.5 to 82.2%, achieving the highest reported average score on
this dataset.