IEEE J Biomed Health Inform. 2022 Feb;26(2):786-797. doi: 10.1109/JBHI.2021.3087735. Epub 2022 Feb 4.
Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, wepropose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly adapt to heterogeneous tumors. We introduce a simple but effective Exponential Distance Transform (ExpDT) that converts user interactions into guide maps regarded as the spatial and appearance prior. Comparing with popular Euclidean and geodesic distances, ExpDT is more robust to various image sizes, which reserves the distribution of interactive inputs. Furthermore, to enhance the tumor-related features, we design a deep interactive module to propagate the guides into deeper layers. We train and evaluate DINs on three MRI data sets from NF1 patients. The experiment results yield significant improvements of 44% and 14% in DSC comparing with automated and other interactive methods, respectively. We also experimentally demonstrate the efficiency of DINs in reducing user burden when comparing with conventional interactive methods.
神经纤维瘤病 1 型（NF1）是一种常染色体显性肿瘤易感性综合征，涉及中枢和外周神经系统。准确检测和分割神经纤维瘤对于评估肿瘤负担和纵向肿瘤大小变化至关重要。自动卷积神经网络（CNN）对肿瘤在 MRI 上的可变解剖位置和异质外观很敏感且脆弱。在这项研究中，我们提出了深度交互网络（DIN）来解决上述限制。用户交互指导模型识别复杂的肿瘤，并快速适应异质肿瘤。我们引入了一种简单而有效的指数距离变换（ExpDT），将用户交互转换为引导图，视为空间和外观先验。与流行的欧几里得和测地线距离相比，ExpDT 对各种图像大小更稳健，保留了交互输入的分布。此外，为了增强与肿瘤相关的特征，我们设计了一个深度交互模块，将引导图传播到更深的层。我们在来自 NF1 患者的三个 MRI 数据集上训练和评估 DINs。实验结果表明，与自动化方法和其他交互方法相比，在 DSC 上分别提高了 44%和 14%。与传统的交互方法相比，我们还通过实验证明了 DINs 在减少用户负担方面的效率。
