目的 采用不同染色方法获得的周围神经标本经过MicroCT扫描后，会获得不同效果的神经断层扫描图像。本文针对饱和氯化钙染色、无染色方法获得的两种周围神经MicroCT图像，提出一种通用的方法，实现不同染色方法获得的周围神经MicroCT图像在统一架构下的神经束轮廓获取。方法 首先设计通用方法架构，构建图像数据集，完成图像标注、分组等关键性的准备环节。然后将迁移学习算法与蒙皮区域卷积神经网络（mask R-CNN）算法结合起来，设计通用方法中的识别模型。最后设计多组实验，采用不同分组的图像数据集对通用方法进行训练、测试，以验证通用方法的效果。结果 通用方法对不同分组的图像数据集的神经束轮廓获取准确率均超过95%，交并比均在86%以上，检测时间均小于0.06 s。此外，对于神经束轮廓信息较复杂的图像数据集，迁移学习结合mask R-CNN的识别模型与纯mask R-CNN的识别模型相比较，准确率和交并比分别提高了5.5% 9.8%和2.4% 3.2%。结论 实验结果表明，针对不同染色方法获得的周围神经MicroCT图像，采用本文方法可以准确、快速、全自动获取得到神经束轮廓。此外，经过迁移后的mask R-CNN能显著提高神经束轮廓获取的准确性和鲁棒性。

Objective Peripheral nerve injury can result in severe paralysis and dysfunction. For a long time, repairing and regenerating injured peripheral nerves have been urgent goals. Detailed intraneural spatial information can be provided by the 3D visualization of fascicular groups in the peripheral nerve. Suitable surgical methods must be selected to repair clinical peripheral nerve defects. The contour information of peripheral nerve MicroCT image is the basis of peripheral nerve 3D reconstruction and visualization. Obtaining the contour information of the fascicular groups is a key step during 3D nerve visualization. In the previous research, the MicroCT images of peripheral nerve were obtained. The foreground and background of these images were relatively different when the images came from samples stained by different methods, such as dyed or not dyed with calcium chloride. If previous segmentation approaches were used to extract the contours of fascicular groups, then various labor-intensive feature extracting and recognition methods had to be applied, and the results were inconsistent. An assistance methodology using image processing can improve the accuracy of obtaining the contour information of fascicular groups. Thus, this study analyzes graph cut theory and algorithm and proposes a generic framework to obtain numerous consistent results easily. The proposed algorithm can be used to assist in neurosurgery diagnosis and has great clinical application value. In the generic framework, the MicroCT images from different dyed samples are processed by the same algorithm, which results in consistent and accurate extracted contours of fascicular groups. Method This method is based on deep learning. The method can automatically extract instinct features from image data and instantly analyze images, can effectively improve detection efficiency, and can be applied to complex images. Mask region convolution neural network (mask R-CNN) is used to extract the contour information from peripheral nerve MicroCT images. Given the impressive achievement of Mask R-CNN for object segmentation, the accuracy of recognition and classification at the pixel level is greatly improved. First, the structure of the generic framework is designed, and the image datasets are constructed. Several key preparations are performed, such as image annotation and grouping. The dataset of images is divided into two groups at a ratio of 3:1, namely, training and test sets. On the basis of the dyed method of the MicroCT images from peripheral nerve, the training and test datasets have three subsets, namely, calcium chloride-dyed image dataset (subset 1), nondyed image dataset (subset 2), and mixed image dataset (subset 3). Second, the principle of mask R-CNN is analyzed, and the generic frameworks of image classification and segmentation are designed, combining mask R-CNN with transfer learning. Even though mask R-CNN is efficient in common segmentation task, it has several limitations. In normal task, mask R-CNN often needs many images to train. However, the datasets of the MicroCT images of peripheral nerves do not have sufficient number of images to train mask R-CNN. Thus, mask R-CNN cannot be used to extract the contour of fascicular groups directly from the MicroCT images of peripheral nerves. Therefore, the transfer learning strategy is combined with mask R-CNN to solve the problem. The training parameters of neural network structure are adjusted manually. mask R-CNN is pretrained by the COCO dataset. mask R-CNN is transferred to the dataset of peripheral nerves for further learning to improve the extracting accuracy of the contour of the fascicular groups. Finally, the target segmentation model based on the contour information of peripheral nerve MicroCT image tasks is constructed. Third, the generic framework is trained and testified by image datasets from different groups. This finding indicates that a highly effective segmentation effect can be achieved. Result All experiments are accomplished in the GTX1070-8G environment. The experimental data are derived from a 5 cm peripheral nerve segment. The peripheral nerve sample is transected into 3 mm segments at -20℃. These segments are used for calcium chloride-dyed and nondyed methods to facilitate the discrimination of different fascicular groups in MicroCT images. The scan sequence from the calcium chloride-dyed method includes 228 images. The scan sequence from the nondyed method includes 523 images. The training dataset in each dataset is used to train the parameters of the neural network structure, and the test dataset is used to test the actual segmentation results. The model execution took 15 000 iterations for the training process. Experiment results show that the pixel average precision of all types of datasets exceeds 95%, and the segmentation accuracy is high, which is highly consistent with the results of artificial segmentation. The intersection over union exceeds 86%, and the mean time to detect is less than 0.06 s, which satisfies the real-time requirements. In comparison with the original mask R-CNN, the proposed framework achieved improved performance, increased average precision by approximately 5.5%-9.8%, and increased intersection over union by approximately 2.4%3.2%. Conclusion Theoretical analysis and experiment results justify the feasibility of the proposed framework. On the basis of the experimental dataset, our training set is relatively limited, but the experiments show that the proposed approach can accurately, rapidly, and automatically extract the contours of fascicular groups. Furthermore, the accuracy, segmentation effect, and robustness are improved greatly when mask R-CNN is combined with transfer learning. The framework can be widely used to segment various MicroCT images, which come from different dyed samples. The auto-mated segmentation of the MicroCT images of peripheral nerves has substantial clinical values. Finally, we discuss the challenges in this generic framework and several unsolved problems.