  Radiation therapy (RT) is widely employed in the clinic for the treatment of
head and neck (HaN) cancers. An essential step of RT planning is the accurate
segmentation of various organs-at-risks (OARs) in HaN CT images. Nevertheless,
segmenting OARs manually is time-consuming, tedious, and error-prone
considering that typical HaN CT images contain tens to hundreds of slices.
Automated segmentation algorithms are urgently required. Recently,
convolutional neural networks (CNNs) have been extensively investigated on this
task. Particularly, 3D CNNs are frequently adopted to process 3D HaN CT images.
There are two issues with na\"ive 3D CNNs. First, the depth resolution of 3D CT
images is usually several times lower than the in-plane resolution. Direct
employment of 3D CNNs without distinguishing this difference can lead to the
extraction of distorted image features and influence the final segmentation
performance. Second, a severe class imbalance problem exists, and large organs
can be orders of times larger than small organs. It is difficult to
simultaneously achieve accurate segmentation for all the organs. To address
these issues, we propose a novel hybrid CNN that fuses 2D and 3D convolutions
to combat the different spatial resolutions and extract effective edge and
semantic features from 3D HaN CT images. To accommodate large and small organs,
our final model, named OrganNet2.5D, consists of only two instead of the
classic four downsampling operations, and hybrid dilated convolutions are
introduced to maintain the respective field. Experiments on the MICCAI 2015
challenge dataset demonstrate that OrganNet2.5D achieves promising performance
compared to state-of-the-art methods.
