Digital Image Analysis with Fully Connected Convolutional Neural Network to Facilitate Hysteroscopic Fibroid Resection

Aims: The study aimed to determine the accuracy of deep neural network in identifying the plane between myoma and normal myometrium. Methods: On the images of surgery, different structures were signed and annotated for the training phase. After the appropriate training of the deep neural network with 4,688 images from that training set, 1,600 formerly unseen images were used for testing. Indication for surgery was heavy menstrual bleeding and hysteroscopic finding was submucous fibroid. Operative intervention was fibroid resection. Recorded videos of transcervical resection of myoma in 13 cases were used for the study. Different filters and procedures were applied by the fully convolutional neural network (FCNN) for identifying previously annotated structures. Results: Previously manually annotated images and the manually drawn bitmasks were used for training the applied FCNN and then this pre-trained network was used for automatic segmentation of normal myometrium in an unseen video frame. The segmentation pixel-wise accuracy achieved the 86.19% considering the Hausdorff metric. Conclusion: Using deep learning technique in analyzing process of endoscopic video frame could help in real-time identification of structures while performing endoscopic surgery. (c) 2018 S. Karger AG, Basel