Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3573-3576. doi: 10.1109/EMBC46164.2021.9630332.
There is evidence that cochlear MR signal intensity may be useful in prognosticating the risk of hearing loss after middle cranial fossa (MCF) resection of acoustic neuroma (AN), but the manual segmentation of this structure is difficult and prone to error. This hampers both large-scale retrospective studies and routine clinical use of this information. To address this issue, we present a fully automatic method that permits the segmentation of the intra-cochlear anatomy in MR images, which uses a weighted active shape model we have developed and validated to segment the intra-cochlear anatomy in CT images. We take advantage of a dataset for which both CT and MR images are available to validate our method on 132 ears in 66 high-resolution T2-weighted MR images. Using the CT segmentation as ground truth, we achieve a mean Dice (DSC) value of 0.81 and 0.79 for the scala tympani (ST) and the scala vestibuli (SV), which are the two main intracochlear structures.Clinical Relevance- The proposed method is accurate and fully automated for MR image segmentation. It can be used to support large retrospective studies that explore relations between MR signal in preoperative images and outcomes. It can also facilitate the routine and clinical use of this information.
有证据表明，耳蜗磁共振（MR）信号强度可能有助于预测听神经瘤（AN）经中颅窝（MCF）切除后听力损失的风险，但这种结构的手动分割很困难且容易出错。这既妨碍了大规模的回顾性研究，也妨碍了该信息的常规临床应用。为了解决这个问题，我们提出了一种完全自动的方法，可以对 MR 图像中的耳蜗内解剖结构进行分割，该方法使用我们开发和验证的加权主动形状模型来对 CT 图像中的耳蜗内解剖结构进行分割。我们利用一个同时提供 CT 和 MR 图像的数据集，在 66 张高分辨率 T2 加权 MR 图像中的 132 只耳朵上验证了我们的方法。使用 CT 分割作为金标准，我们在鼓阶（ST）和前庭阶（SV）两个主要的耳蜗内结构上实现了平均骰子系数（DSC）值为 0.81 和 0.79。
临床相关性- 所提出的方法对于 MR 图像分割是准确且全自动的。它可用于支持探索术前图像中 MR 信号与结果之间关系的大型回顾性研究。它还可以促进该信息的常规和临床应用。
