DOI: 10.57760/sciencedb.33909
title: Anatomical classification dataset of the inferior mesenteric artery during No.253 lymph node dissection in robotic-assisted anterior resection for rectal cancer
title: Anatomical classification dataset of the inferior mesenteric artery during No.253 lymph node dissection in robotic-assisted anterior resection for rectal cancer

description: Globally, colorectal cancer ranks second in terms of death and third in terms of incidence. Rectal cancer accounts for 60–75% of cases, and its incidence is similar to that of colon cancer. The main treatment for rectal cancer is still surgical excision. In China, as well as by some surgeons in Europe and America, robotic surgery has been widely used to treat colorectal cancer. However, dissecting and maintaining the inferior mesenteric vessels during surgery is extremely difficult due to their varied vascular patterns. To guarantee oncological safety and to protect arteries and nerves, the inferior mesenteric artery (IMA) must be precisely separated during the dissection of the No.253 lymph node. For safe intraoperative navigation, prevention of postoperative problems, and the optimization of surgical operations, an accurate evaluation of anatomical changes in the inferior mesenteric arteries and their branches is essential. The dynamic vascular structure of this area is still lacking in publicly accessible, robotic-assisted, annotated datasets. This limits the advancement of artificial intelligence (AI) and computer vision systems for surgical assistance. We provide the IMA anatomical classification dataset for robotic-assisted anterior resection for rectal cancer (RAAR-IMAC-30), also referred to as the RENJI-RC-01 Dataset, to address this problem. 3,017 annotated frames from 30 complete surgical videos are included in the collection. 10 key vessels from 4 anatomical IMA types (Type 0–3) and 4 procedures (exposure, dissection, ligation, and transection) are annotated in detail at the pixel level. Dice coefficients and the 95th Hausdorff Distance were used to validate the consistency of each annotation. The RAAR-IMAC-30 dataset facilitates better anatomical recognition, surgical training, and intraoperative decision-making in rectal cancer surgery and serves as a fundamental resource for creating AI-driven surgical navigation systems.
description: Globally, colorectal cancer ranks second in terms of death and third in terms of incidence. Rectal cancer accounts for 60–75% of cases, and its incidence is similar to that of colon cancer. The main treatment for rectal cancer is still surgical excision. In China, as well as by some surgeons in Europe and America, robotic surgery has been widely used to treat colorectal cancer. However, dissecting and maintaining the inferior mesenteric vessels during surgery is extremely difficult due to their varied vascular patterns. To guarantee oncological safety and to protect arteries and nerves, the inferior mesenteric artery (IMA) must be precisely separated during the dissection of the No.253 lymph node. For safe intraoperative navigation, prevention of postoperative problems, and the optimization of surgical operations, an accurate evaluation of anatomical changes in the inferior mesenteric arteries and their branches is essential. The dynamic vascular structure of this area is still lacking in publicly accessible, robotic-assisted, annotated datasets. This limits the advancement of artificial intelligence (AI) and computer vision systems for surgical assistance. We provide the IMA anatomical classification dataset for robotic-assisted anterior resection for rectal cancer (RAAR-IMAC-30), also referred to as the RENJI-RC-01 Dataset, to address this problem. 3,017 annotated frames from 30 complete surgical videos are included in the collection. 10 key vessels from 4 anatomical IMA types (Type 0–3) and 4 procedures (exposure, dissection, ligation, and transection) are annotated in detail at the pixel level. Dice coefficients and the 95th Hausdorff Distance were used to validate the consistency of each annotation. The RAAR-IMAC-30 dataset facilitates better anatomical recognition, surgical training, and intraoperative decision-making in rectal cancer surgery and serves as a fundamental resource for creating AI-driven surgical navigation systems.