PDF(Pubmed)
Abstract：
Acoustic neuroma is one of the most common tumors in the cerebellopontine angle area. Patients with acoustic neuroma have clinical manifestations of the cerebellopontine angle occupying syndrome, such as tinnitus, hearing impairment and even hearing loss. Acoustic neuromas often grow in the internal auditory canal. Neurosurgeons need to observe the lesion contour with the help of MRI images, which not only takes a lot of time, but also is easily affected by subjective factors. Therefore, the automatic and accurate segmentation of acoustic neuroma in cerebellopontine angle on MRI is of great significance for surgical treatment and expected rehabilitation. In this paper, an automatic segmentation method based on Transformer is proposed, using TransUNet as the core model. As some acoustic neuromas are irregular in shape and grow into the internal auditory canal, larger receptive fields are thus needed to synthesize the features. Therefore, we added Atrous Spatial Pyramid Pooling to CNN, which can obtain a larger receptive field without losing too much resolution. Since acoustic neuromas often occur in the cerebellopontine angle area with relatively fixed position, we combined channel attention with pixel attention in the up-sampling stage so as to make our model automatically learn different weights by adding the attention mechanism. In addition, we collected 300 MRI sequence nuclear resonance images of patients with acoustic neuromas in Tianjin Huanhu hospital for training and verification. The ablation experimental results show that the proposed method is reasonable and effective. The comparative experimental results show that the Dice and Hausdorff 95 metrics of the proposed method reach 95.74% and 1.9476 mm respectively, indicating that it is not only superior to the classical models such as UNet, PANet, PSPNet, UNet++, and DeepLabv3, but also show better performance than the newly-proposed SOTA (state-of-the-art) models such as CCNet, MANet, BiseNetv2, Swin-Unet, MedT, TransUNet, and UCTransNet.
摘要：
听神经瘤是桥小脑角区最常见的肿瘤之一。听神经瘤患者有桥小脑角占位性综合征的临床表现,比如耳鸣,听力受损甚至听力损失。听神经瘤通常生长在内耳道。神经外科医生需要借助MRI图像观察病灶轮廓,这不仅需要很多时间，但也容易受到主观因素的影响。因此,MRI上桥脑小脑角听神经瘤的自动准确分割对手术治疗和预期康复具有重要意义。在本文中,提出了一种基于Transformer的自动分割方法，使用TransUNet作为核心模型。由于一些听神经瘤形状不规则，并长入内耳道，因此，需要更大的感受场来合成这些特征。因此,我们在CNN增加了Atrous空间金字塔池，可以获得更大的感受野，而不会损失太多分辨率。由于听神经瘤通常发生在位置相对固定的小脑桥脑角区域，在上采样阶段，我们将通道注意力与像素注意力相结合，从而使我们的模型通过添加注意力机制自动学习不同的权重。此外,我们收集了天津环湖医院300例听神经瘤患者的MRI序列核磁共振图像进行培训和验证。烧蚀实验结果表明了该方法的合理性和有效性。对比实验结果表明,该方法的Dice和Hausdorff95指标分别达到95.74%和1.9476mm,表明它不仅优于UNet等经典模型，PANet,PSPNet,UNet++,和DeepLabv3，但也显示出比新提出的SOTA（最先进的）模型（如CCNet）更好的性能，MANet,BiseNetv2,Swin-Unet,MedT,TransUNet,和UCTRANNet。
