IEEE Trans Image Process. 2021;30:739-753. doi: 10.1109/TIP.2020.3038363. Epub 2020 Dec 4.
The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline as the first fully automated algorithm for segmenting multiple temporal bone structures from CT volumes, including the sigmoid sinus, facial nerve, inner ear, malleus, incus, stapes, internal carotid artery and internal auditory canal. The proposed fully convolutional network, PWD-3DNet, is a patch-wise densely connected (PWD) three-dimensional (3D) network. The accuracy and speed of the proposed algorithm was shown to surpass current manual and semi-automated segmentation techniques. The experimental results yielded significantly high Dice similarity scores and low Hausdorff distances for all temporal bone structures with an average of 86% and 0.755 millimeter (mm), respectively. We illustrated that overlapping in the inference sub-volumes improves the segmentation performance. Moreover, we proposed augmentation layers by using samples with various transformations and image artefacts to increase the robustness of PWD-3DNet against image acquisition protocols, such as smoothing caused by soft tissue scanner settings and larger voxel sizes used for radiation reduction. The proposed algorithm was tested on low-resolution CTs acquired by another center with different scanner parameters than the ones used to create the algorithm and shows potential for application beyond the particular training data used in the study.
颞骨是侧颅表面的一部分，包含负责听觉和平衡的器官。由于这种复杂而微观的三维解剖结构，掌握颞骨手术具有挑战性。基于计算机断层扫描 (CT) 图像对颞骨内部解剖结构进行分割对于手术培训和排练等应用是必要的。然而，由于关键结构之间的强度相似且解剖关系复杂、标准临床 CT 上无法检测到小结构以及手动分割所需的时间，颞骨分割具有挑战性。本文描述了一种基于单类深度学习的流水线，作为第一个从 CT 体数据中自动分割多个颞骨结构的完全自动化算法，包括乙状窦、面神经、内耳、锤骨、砧骨、镫骨、颈内动脉和内耳道。所提出的完全卷积网络 PWD-3DNet 是一种基于补丁的密集连接 (PWD) 三维 (3D) 网络。所提出算法的准确性和速度被证明超过了当前的手动和半自动分割技术。实验结果表明，所有颞骨结构的 Dice 相似性得分和 Hausdorff 距离都非常高，平均分别为 86%和 0.755 毫米 (mm)。我们表明，在推断子体积中的重叠可以提高分割性能。此外，我们通过使用具有各种变换和图像伪影的样本提出了扩充层，以提高 PWD-3DNet 对图像采集协议的鲁棒性，例如软组织扫描仪设置引起的平滑和为减少辐射而使用的较大体素尺寸。所提出的算法在由与创建算法不同的扫描仪参数获取的低分辨率 CT 上进行了测试，并且显示出在研究中使用的特定训练数据之外应用的潜力。
