A reference-based approach for tumor size estimation in monocular laparoscopic videos

Laparoscopic exploration of the abdominal cavity is rou tinely performed for the diagnosis, assessment, and staging of peritoneal metastasis (PM). Accurately measuring tumor size during this procedure is crucial for prognosis and treatment planning. As conventional approaches for tumor size measurement rely on subjective manual assessments during or after surgery, they stand to benefit from computer assistance. This study proposes a new method for measuring tumor size in laparoscopic monocular videos. Specifically, we introduce a novel mathematical equation that connects the intrinsic parameters of a monocular camera, the surface area of target and reference objects, and their distances to the camera. Furthermore, we combine this equation with an object segmentation model (Mask2Former) and a depth estimation model (MiDaS), creating an end-to-end framework that automates tumor size measurement in monocular laparoscopic videos. We evaluate the proposed method using a laparoscopy dataset comprising 18 videos depicting 76 tumor biopsies, with tumor size measured by surgeons who are experts in laparoscopic surgery. When estimating the size of the various tumors in this dataset, we obtain a Mean Absolute Error (MAE) of 2.44 mm ± 0.23 mm, demonstrating that the newly proposed method accurately predicts intraoperative tumor size. Our code and the evaluation dataset are publicly available on https://github.com/amiiiirrrr/TSEMLV