Purpose

Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard.

Methods

Using high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy.

Results

We found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, \(p<0.001\)), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation.

Conclusion

We suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.

中文翻译：

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手动分割与半自动分割在 MRI 上量化前庭神经鞘瘤体积。

目的

前庭神经鞘瘤 (VS) 的治疗基于在 T1 MRI 扫描和造影剂注射中观察到的肿瘤大小。目前的临床实践是测量肿瘤最大尺寸的直径。已经表明，体积测量作为 VS 尺寸的测量更准确、更可靠。实现这种体积测量的参考方法是手动分割肿瘤，这是一项耗时的任务。我们建议半自动分割可能是该问题的临床适用解决方案，并且它可以取代线性测量作为临床标准。

方法

使用可用于学术目的的高质量软件，我们与 5 位临床医生和科学家进行了手动与半自动 MRI 上 VS 分割的比较研究。我们收集了定量和定性数据来比较这两种方法；包括分割时间、分割工作量和分割精度。

结果

我们发现，与手动分割相比，所选择的半自动分割方法明显更快（167 s vs 479 s，\(p<0.001\)），对时间和物理的要求更低，并且具有大致相同的性能，并且在准确性方面有所提高. 与手动分割相比，存在一些限制，包括算法的不可预测性和错误，这会产生更多的挫败感和更多的脑力劳动。

结论

我们建议半自动分割可以在临床上应用于 MRI 上 VS 的体积测量。将来，可以改进通用软件以专门用于 VS 分割，从而提高准确性。