Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet

Zhuo Zhang; Xiaochen Zhang; Yong Yang; Jieyu Liu; Chenzi Zheng; Hua Bai; Quanfeng Ma

doi:10.3389/fnins.2023.1207149

Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet

Front Neurosci. 2023 May 24:17:1207149. doi: 10.3389/fnins.2023.1207149. eCollection 2023.

Authors

Zhuo Zhang¹, Xiaochen Zhang², Yong Yang³, Jieyu Liu¹, Chenzi Zheng⁴, Hua Bai¹, Quanfeng Ma²

Affiliations

¹ Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China.
² Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, China.
³ School of Computer Science and Technology, Tiangong University, Tianjin, China.
⁴ College of Foreign Languages, Nankai University, Tianjin, China.

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.

Keywords: UNet; acoustic neuroma; deep learning; image segmentation; transformer.