Deep learning-based vessel automatic recognition for laparoscopic right hemicolectomy.
Surg Endosc
; 38(1): 171-178, 2024 01.
Article
em En
| MEDLINE
| ID: mdl-37950028
ABSTRACT
BACKGROUND:
In laparoscopic right hemicolectomy (RHC) for right-sided colon cancer, accurate recognition of the vascular anatomy is required for appropriate lymph node harvesting and safe operative procedures. We aimed to develop a deep learning model that enables the automatic recognition and visualization of major blood vessels in laparoscopic RHC. MATERIALS ANDMETHODS:
This was a single-institution retrospective feasibility study. Semantic segmentation of three vessel areas, including the superior mesenteric vein (SMV), ileocolic artery (ICA), and ileocolic vein (ICV), was performed using the developed deep learning model. The Dice coefficient, recall, and precision were utilized as evaluation metrics to quantify the model performance after fivefold cross-validation. The model was further qualitatively appraised by 13 surgeons, based on a grading rubric to assess its potential for clinical application.RESULTS:
In total, 2624 images were extracted from 104 laparoscopic colectomy for right-sided colon cancer videos, and the pixels corresponding to the SMV, ICA, and ICV were manually annotated and utilized as training data. SMV recognition was the most accurate, with all three evaluation metrics having values above 0.75, whereas the recognition accuracy of ICA and ICV ranged from 0.53 to 0.57 for the three evaluation metrics. Additionally, all 13 surgeons gave acceptable ratings for the possibility of clinical application in rubric-based quantitative evaluations.CONCLUSION:
We developed a DL-based vessel segmentation model capable of achieving feasible identification and visualization of major blood vessels in association with RHC. This model may be used by surgeons to accomplish reliable navigation of vessel visualization.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Laparoscopia
/
Neoplasias do Colo
/
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
Surg Endosc
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
GASTROENTEROLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão