A nested parallel multiscale convolution for cerebrovascular segmentation

• Published in: Medical physics (Lancaster) 2021-12, Vol.48 (12), p.7971-7983
• Main Authors: Xia, Likun, Xie, Yixuan, Wang, Qiwang, Zhang, Hao, He, Cheng, Yang, Xiaonan, Lin, Jinghui, Song, Ran, Liu, Jiang, Zhao, Yitian
• Format: Article
• Language: English
• Subjects: cerebrovascular segmentation; Image Processing, Computer-Assisted; Magnetic Resonance Angiography; Magnetic Resonance Imaging; multiscale feature extraction; Neural Networks, Computer; U‐Net
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.15280

Purpose: Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular diseases. Many segmentation frameworks based on convolutional neural networks (CNNs) or U‐Net‐like structures have been proposed for cerebrovascular segmentation. Unfortunately, the segmentation results are still unsatisfactory, particularly in the small/thin cerebrovascular due to the following reasons: (1) the lack of attention to multiscale features in encoder caused by the convolutions with single kernel size; (2) insufficient extraction of shallow and deep‐seated features caused by the depth limitation of transmission path between encoder and decoder; (3) insufficient utilization of the extracted features in decoder caused by less attention to multiscale features. Methods: Inspired by U‐Net++, we propose a novel 3D U‐Net‐like framework termed Usception for small cerebrovascular. It includes three blocks: Reduction block, Gap block, and Deep block, aiming to: (1) improve feature extraction ability by grouping different convolution sizes; (2) increase the number of multiscale features in different layers by grouping paths of different depths between encoder and decoder; (3) maximize the ability of decoder in recovering multiscale features from Reduction and Gap block by using convolutions with different kernel sizes. Results: The proposed framework is evaluated on three public and in‐house clinical magnetic resonance angiography (MRA) data sets. The experimental results show that our framework reaches an average dice score of 69.29%, 87.40%, 77.77% on three data sets, which outperform existing state‐of‐the‐art methods. We also validate the effectiveness of each block through ablation experiments. Conclusions: By means of the combination of Inception‐ResNet and dimension‐expanded U‐Net++, the proposed framework has demonstrated its capability to maximize multiscale feature extraction, thus achieving competitive segmentation results for small cerebrovascular.