卷积神经网络用于在单张MRI切片上检测前庭神经鞘瘤：一项可行性研究。

Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study.

作者信息

Koechli Carole, Vu Erwin, Sager Philipp, Näf Lukas, Fischer Tim, Putora Paul M, Ehret Felix, Fürweger Christoph, Schröder Christina, Förster Robert, Zwahlen Daniel R, Muacevic Alexander, Windisch Paul

机构信息

Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland.

Department of Radiation Oncology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland.

出版信息

Cancers (Basel). 2022 Apr 20;14(9):2069. doi: 10.3390/cancers14092069.

DOI:10.3390/cancers14092069

PMID:35565199

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9104481/

Abstract

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935-0.963) for the internal validation and 0.912 (95% CI 0.866-0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures.

摘要

在本研究中，我们旨在通过使用二维卷积神经网络（2D-CNN）在个体磁共振成像（MRI）切片中检测前庭神经鞘瘤（VSs）。使用来自一个机构的对比增强T1加权（T1c）MRI切片对预训练的卷积神经网络（ResNet-34）进行再训练并进行内部验证。在第二步中，使用来自不同机构的T1c和T1加权（T1）切片对模型进行外部验证。作为替代，使用了包含部分单侧VS的整个横向切片中有无肿瘤的对分切片。基于分类准确率和混淆矩阵评估模型预测。分别共有539、94和74例患者纳入训练、内部验证和外部T1c验证。这导致内部验证的准确率为0.949（95%CI 0.935 - 0.963），外部T1c验证的准确率为0.912（95%CI 0.866 - 0.958）。我们认为，由于对计算能力的需求降低且无需分割，二维卷积神经网络在某些任务中可能是2.5/三维卷积神经网络的一个有前景的替代方案。然而，需要进一步研究二维卷积神经网络与更复杂架构之间的差异。