Automated facial nerve identification in microsurgery with an improved unet.

To develop a deep-learning model that improves the segmentation and detection of Facial Nerve in microsurgery, thereby increasing surgical precision and safety. We collected videos from 25 patients undergoing facial nerve decompression microsurgery. From these videos, we extracted and annotated 2724 images from 14 patients for training and validation (training set: validation set = 2452: 272). Data augmentation techniques were applied to the training set with a five-fold increase (12,260 images). To evaluate the accuracy of our model, we carefully selected and annotated 1674 images from 11 patients who had not been previously trained. We then introduced an Improved Unet model that integrates various attention mechanisms, a feature-rich skip connection mechanism, and a multi-dimensional convolutional block to overcome the challenges faced by traditional Unet models when dealing with blurred or small target images. Compared with the state-of-the-art method, our proposed model achieved the best performance. The FullGrad-generated heatmap certified that the model has learned the Facial Nerve features. The Improved Unet obtained an mIOU of 0.9165 with the validation set and an mIOU of 0.6543 with the test set. In various complex microsurgical environments including blood, occlusion, and blurriness, our model can detect and segment Facial Nerve precisely. The results demonstrate the proposed method is highly promising in providing real-time intraoperative guidance of the critical anatomical structures in microsurgery.