  Accurate and automatic organ segmentation from 3D radiological scans is an
important yet challenging problem for medical image analysis. Specifically, the
pancreas demonstrates very high inter-patient anatomical variability in both
its shape and volume. In this paper, we present an automated system using 3D
computed tomography (CT) volumes via a two-stage cascaded approach: pancreas
localization and segmentation. For the first step, we localize the pancreas
from the entire 3D CT scan, providing a reliable bounding box for the more
refined segmentation step. We introduce a fully deep-learning approach, based
on an efficient application of holistically-nested convolutional networks
(HNNs) on the three orthogonal axial, sagittal, and coronal views. The
resulting HNN per-pixel probability maps are then fused using pooling to
reliably produce a 3D bounding box of the pancreas that maximizes the recall.
We show that our introduced localizer compares favorably to both a conventional
non-deep-learning method and a recent hybrid approach based on spatial
aggregation of superpixels using random forest classification. The second,
segmentation, phase operates within the computed bounding box and integrates
semantic mid-level cues of deeply-learned organ interior and boundary maps,
obtained by two additional and separate realizations of HNNs. By integrating
these two mid-level cues, our method is capable of generating
boundary-preserving pixel-wise class label maps that result in the final
pancreas segmentation. Quantitative evaluation is performed on a publicly
available dataset of 82 patient CT scans using 4-fold cross-validation (CV). We
achieve a Dice similarity coefficient (DSC) of 81.27+/-6.27% in validation,
which significantly outperforms previous state-of-the art methods that report
DSCs of 71.80+/-10.70% and 78.01+/-8.20%, respectively, using the same dataset.
