Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size - Zhu, Guangyu; Luo, Xueqi; Yang, Tingting; Cai, Li; Yeo, Joon Hock; Yan, Ge; Yang, Jian (Frontiers in Physiology, 2022)
The manual identification and segmentation of IAs involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction became a research frontier. In this study, a deep-learning-based framework for IA indetifacation and segmentation were developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework’s performance were investigated. Three 3D segmentation-dedicated CNN architectures, including 3D UNet, VNet and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial CTA images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the CNN mentioned above. The performances of three CNNs were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitayive metrics. All the CNNs showed a none-zero voxel-wise recall in case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ±0.140 (3.5 % and 17.3 % higher than that of VNet and 3D Res-UNet), as well as an average DSC of 0.818 ± 0.100, which was 4.1 %, and 11.7 % higher than VNet and 3D Res-UNet. Moreover, the average HD of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3 % and 17.3 % lower than that of VNet and 3D Res-UNet. The 3D Deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +2.1230/-3.1840 mm, an average distance of 0.3454 mm, a STD of 0.7098 mm, a RMS of 0.8383 mm. In addition, the average segmentation time of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.