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.

翻译：在诊所广泛使用辐射治疗(RT)治疗头部和颈部癌症(HAN)。RT计划的一个重要步骤是将各种风险器官(OARs)精确分解到HAN CT图像中。然而,将OARs人工分解是耗时、乏味和容易出错的,因为典型的HAN CT图像含有数十至数百片片段。 迫切需要自动分解算法。 最近,对这项任务进行了广泛的调查。 特别是, 3D CNN经常被采纳到处理 3D HAN 的网络CT 图像中。 与 NA\ “ 3D CNN ” 相关图像中存在两个问题。 首先, 3D CT 的深度分辨率通常比平面分辨率低几倍。 直接使用 3D CNN 图像可以提取扭曲的图像特征并影响最终的分解性表现。 其次, 严重的级不平衡问题可能比小器官的顺序要大得多。 与 NCD 3 DNA 的深度和跨层图像显示, 我们很难同时实现 3 IM压 。