Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery. Issue 5 (6th April 2022)

• Record Type: Journal Article
• Authors: Igaki, Takahiro; Kitaguchi, Daichi; Kojima, Shigehiro; Hasegawa, Hiro; Takeshita, Nobuyoshi; Mori, Kensaku; Kinugasa, Yusuke; Ito, Masaaki
• Is Part Of: Diseases of the colon & rectum. Volume 65:Issue 5(2022)
• Page Start: e329
• Page End: e333
• Publication Date: 2022-04-06
• Subjects: Artificial intelligence -- Computer vision -- Intraoperative image-guided navigation -- Laparoscopic colorectal surgery -- Semantic segmentation -- Total mesorectal excision; Colon (Anatomy) -- Diseases -- Periodicals; Rectum -- Diseases -- Periodicals; Colonic Diseases -- Periodicals; Colorectal Surgery -- Periodicals; 616.34
• DOI: 10.1097/DCR.0000000000002393
• Languages: English
• ISSNs: 0012-3706

Abstract

Abstract : BACKGROUND: Total mesorectal excision is the standard surgical procedure for rectal cancer because it is associated with low local recurrence rates. To the best of our knowledge, this is the first study to use an image-guided navigation system with total mesorectal excision. IMPACT OF INNOVATION: The impact of innovation is the development of a deep learning-based image-guided navigation system for areolar tissue in the total mesorectal excision plane. Such a system might be helpful to surgeons because areolar tissue can be used as a landmark for the appropriate dissection plane. TECHNOLOGY, MATERIALS, AND METHODS: This was a single-center experimental feasibility study involving 32 randomly selected patients who had undergone laparoscopic left-sided colorectal resection between 2015 and 2019. Deep learning-based semantic segmentation of areolar tissue in the total mesorectal excision plane was performed. Intraoperative images capturing the total mesorectal excision scene extracted from left colorectal laparoscopic resection videos were used as training data for the deep learning model. Six hundred annotation images were created from 32 videos, with 528 images in the training and 72 images in the test data sets. The experimental feasibility study was conducted at the Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan. Dice coefficient was used to evaluate semantic segmentation accuracy for areolar tissue. PRELIMINARY RESULTS: The developed semantic segmentation model helped locate and highlight the areolar tissue area in the total mesorectal excision plane. The accuracy and generalization performance of deep learning models depend mainly on the quantity and quality of the training data. This study had only 600 images; thus, more images for training are necessary to improve the recognition accuracy. CONCLUSION AND FUTURE DIRECTIONS: We successfully developed a total mesorectal excision plane image-guided navigation system based on an areolar tissue segmentation approach with high accuracy. This may aid surgeons in recognizing the total mesorectal excision plane for dissection.