Computer Assisted Phase Recognition of Micro-Neurosurgical Intraoperative Videos

Neurosurgery is a highly complex, intricate procedure performed by a trained neurosurgeon with a blended aid of radiological imaging and cutting-edge technologies. Neurosurgery is considered a highly challenging and demanding task, and automated recognition of surgical steps from live video feed is expected to aid neurosurgeons in making informed procedural decisions. Besides, the autonomous phase recognition is essential in developing intelligent operating theatres to better the outcome of the surgery, and thereby improve patient’s life post surgery. Phase recognition and characterization facilitates in accounting the overall summary of the surgical procedures and helps in not only assessing the skills of the surgeon but also helps to relate the surgical procedures with the outcome for future reference. Computer vision based phase recognition of the neurosurgery is a step towards making autonomous characterization of the complete surgery and later integrate the same model for the development of context-aware neurosurgical robotic assistant. Deep learning techniques in the form of Dense convolutional neural network followed by an LSTM (Long Short Term Memory) presented phase recognition accuracy of 83.6% which is the best among the state-of-the-art (SOTA) feature extractor methods available. Acceptable inference rate was characterized for the live microscopic-surgical videos, which satisfies the needs of neurosurgical practices.