Computed Tomography (CT) has become an important way for examining the critical anatomical organs of the human temporal bone in the diagnosis and treatment of ear diseases. Segmentation of the critical anatomical organs is an important fundamental step for the computer assistant analysis of human temporal bone CT images. However, it is challenging to segment sophisticated and small organs. To deal with this issue, a novel 3D Deep Supervised Densely Network (3D-DSD Net) is proposed in this paper. The network adopts a dense connection design and a 3D multi-pooling feature fusion strategy in the encoding stage of the 3D-Unet, and a 3D deep supervised mechanism is employed in the decoding stage. The experimental results show that our method achieved competitive performance in the CT data segmentation task of the small organs in the temporal bone.

中文翻译：

---

用于CT图像中人类颞骨分割的小器官的3D深度监督密集网络。

计算机断层扫描（CT）已成为检查人类颞骨的关键解剖器官在耳部疾病的诊断和治疗中的重要方法。关键解剖器官的分割是计算机辅助分析人类颞骨CT图像的重要基础步骤。然而，分割复杂的小器官是挑战性的。为了解决这个问题，本文提出了一种新颖的3D深度监督密集网络（3D-DSD Net）。该网络在3D-Unet的编码阶段采用密集连接设计和3D多池特征融合策略，在解码阶段采用3D深度监督机制。实验结果表明，我们的方法在颞骨小器官的CT数据分割任务中取得了竞争优势。