Automatic labelling of tumourous frames in free-hand laparoscopic ultrasound video.

Laparoscopic ultrasound (US) is often used during partial nephrectomy surgeries to identify tumour boundaries within the kidney. However, visual identification is challenging as tumour appearance varies across patients and US images exhibit significant noise levels. To address these challenges, we present the first fully automatic method for detecting the presence of kidney tumour in free-hand laparoscopic ultrasound sequences in near real-time. Our novel approach predicts the probability that a frame contains tumourous tissue using random forests and encodes this probability combined with a regularization term within a graph. Using Dijkstra's algorithm we find a globally optimal labelling (tumour vs. non-tumour) of each frame. We validate our method on a challenging clinical dataset composed of five patients, with a total of 2025 2D ultrasound frames, and demonstrate the ability to detect the presence of kidney tumour with a sensitivity and specificity of 0.774 and 0.916, respectively.