  Precise delineation of organs at risk (OAR) is a crucial task in radiotherapy
treatment planning, which aims at delivering high dose to the tumour while
sparing healthy tissues. In recent years algorithms showed high performance and
the possibility to automate this task for many OAR. However, for some OAR
precise delineation remains challenging. The esophagus with a versatile shape
and poor contrast is among these structures. To tackle these issues we propose
a 3D fully (convolutional neural network (CNN) driven random walk (RW) approach
to automatically segment the esophagus on CT. First, a soft probability map is
generated by the CNN. Then an active contour model (ACM) is fitted on the
probability map to get a first estimation of the center line. The outputs of
the CNN and ACM are then used in addition to CT Hounsfield values to drive the
RW. Evaluation and training was done on 50 CTs with peer reviewed esophagus
contours. Results were assessed regarding spatial overlap and shape
similarities.
  The generated contours showed a mean Dice coefficient of 0.76, an average
symmetric square distance of 1.36 mm and an average Hausdorff distance of 11.68
compared to the reference. These figures translate into a very good agreement
with the reference contours and an increase in accuracy compared to other
methods.
  We show that by employing a CNN accurate estimations of esophagus location
can be obtained and refined by a post processing RW step. One of the main
advantages compared to previous methods is that our network performs
convolutions in a 3D manner, fully exploiting the 3D spatial context and
performing an efficient and precise volume-wise prediction. The whole
segmentation process is fully automatic and yields esophagus delineations in
very good agreement with the used gold standard, showing that it can compete
with previously published methods.
