Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study

Abstract

Background: The preservation of autonomic nerves is the most important factor in maintaining genitourinary function in colorectal surgery; however, these nerves are not clearly recognisable, and their identification is strongly affected by the surgical ability. Therefore, this study aimed to develop a deep learning model for the semantic segmentation of autonomic nerves during laparoscopic colorectal surgery and to experimentally verify the model through intraoperative use and pathological examination.

Materials and methods: The annotation data set comprised videos of laparoscopic colorectal surgery. The images of the hypogastric nerve (HGN) and superior hypogastric plexus (SHP) were manually annotated under a surgeon's supervision. The Dice coefficient was used to quantify the model performance after five-fold cross-validation. The model was used in actual surgeries to compare the recognition timing of the model with that of surgeons, and pathological examination was performed to confirm whether the samples labelled by the model from the colorectal branches of the HGN and SHP were nerves.

Results: The data set comprised 12 978 video frames of the HGN from 245 videos and 5198 frames of the SHP from 44 videos. The mean (±SD) Dice coefficients of the HGN and SHP were 0.56 (±0.03) and 0.49 (±0.07), respectively. The proposed model was used in 12 surgeries, and it recognised the right HGN earlier than the surgeons did in 50.0% of the cases, the left HGN earlier in 41.7% of the cases and the SHP earlier in 50.0% of the cases. Pathological examination confirmed that all 11 samples were nerve tissue.

Conclusion: An approach for the deep-learning-based semantic segmentation of autonomic nerves was developed and experimentally validated. This model may facilitate intraoperative recognition during laparoscopic colorectal surgery.