利用弱监督多任务深度学习在术中 CT 中检测人工耳蜗褶皱

Mohammad M R Khan, Yubo Fan, Benoit M Dawant, Jack H Noble
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引用次数: 0

摘要

在人工耳蜗植入(CI)手术中,通过手术将电极阵列植入耳蜗。电极用于刺激听觉神经,恢复受术者的听觉。如果电极阵列在插入过程中折叠在耳蜗内,可能会导致创伤、残余听力受损和听力恢复不良。术中发现这种情况后,外科医生就可以进行再植入手术。然而,术中检测需要经验,而且电生理测试有时也无法检测到阵列折叠。由于阵列折叠的发生率较低,我们生成了一个带有折叠合成电极阵列和真实金属伪影的 CT 图像数据集。该数据集用于训练多任务定制 3D-UNet 模型,以检测阵列折叠。我们在真实的术后 CT 图像(7 幅有折叠阵列,200 幅无折叠阵列)上测试了训练好的模型。我们的模型可以正确分类所有折叠病例,而仅误分了 3 个非折叠病例。因此,该模型在阵列折叠检测方面大有可为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cochlear Implant Fold Detection in Intra-operative CT Using Weakly Supervised Multi-task Deep Learning.

In cochlear implant (CI) procedures, an electrode array is surgically inserted into the cochlea. The electrodes are used to stimulate the auditory nerve and restore hearing sensation for the recipient. If the array folds inside the cochlea during the insertion procedure, it can lead to trauma, damage to the residual hearing, and poor hearing restoration. Intraoperative detection of such a case can allow a surgeon to perform reimplantation. However, this intraoperative detection requires experience and electrophysiological tests sometimes fail to detect an array folding. Due to the low incidence of array folding, we generated a dataset of CT images with folded synthetic electrode arrays with realistic metal artifact. The dataset was used to train a multitask custom 3D-UNet model for array fold detection. We tested the trained model on real post-operative CTs (7 with folded arrays and 200 without). Our model could correctly classify all the fold-over cases while misclassifying only 3 non fold-over cases. Therefore, the model is a promising option for array fold detection.

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