TY  - JOUR
AU  - Gulzar, Hafsa
AU  - Bilal, Muhammad
AU  - Nawaz, Muhammad Zubair
AU  - Yaqub, Muhammad
AU  - Bunterngchite, Chayut
PY  - 2026
DA  - 2026/09/22
TI  - Deep Learning-Based Segmentation, Action, and Phase Recognition in Laparoscopic Cholecystectomy: A Review
JO  - Journal of Artificial Intelligence in Bioinformatics
T2  - Journal of Artificial Intelligence in Bioinformatics
JF  - Journal of Artificial Intelligence in Bioinformatics
VL  - 2
IS  - 2
SP  - 55
EP  - 67
DO  - 10.62762/JAIB.2026.698089
UR  - https://www.icck.org/article/abs/JAIB.2026.698089
KW  - laparoscopic cholecystectomy
KW  - deep learning
KW  - semantic segmentation
KW  - surgical phase recognition
KW  - surgical action recognition
AB  - The integration of artificial intelligence into biomedical image analysis represents a paradigm shift in clinical decision support and surgical informatics. Laparoscopic cholecystectomy (LC) is a widely performed minimally invasive procedure but remains technically challenging due to limited visibility, anatomical variations, and the risk of complications such as bile duct injury. Artificial intelligence (AI), particularly deep learning (DL), has shown great potential for improving intraoperative understanding through automated surgical video analysis. This review summarizes recent advances in DL-based LC video analysis, focusing on semantic segmentation and surgical action or phase recognition. Existing approaches are categorized into convolutional neural networks (CNNs), transformer-based architectures, and hybrid spatio-temporal models. For segmentation, U-Net variants, DeepLab architectures, and vision transformers are reviewed for identifying anatomical structures and surgical instruments. For action and phase recognition, CNN-LSTM frameworks, 3D CNNs, and video transformers are discussed for modeling surgical workflows and temporal dependencies. We also summarize commonly used datasets, including Cholec80 and CholecSeg8k, and evaluation metrics such as Dice score, IoU, accuracy, and F1-score. Major challenges include data scarcity, inter-surgeon variability, domain shift, and real-time computational constraints. Future research should emphasize self-supervised learning, multi-modal fusion, and clinically explainable AI to improve robustness, generalizability, and real-world surgical deployment.
SN  - 3068-7535
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Gulzar2026Deep,
  author = {Hafsa Gulzar and Muhammad Bilal and Muhammad Zubair Nawaz and Muhammad Yaqub and Chayut Bunterngchite},
  title = {Deep Learning-Based Segmentation, Action, and Phase Recognition in Laparoscopic Cholecystectomy: A Review},
  journal = {Journal of Artificial Intelligence in Bioinformatics},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {55-67},
  doi = {10.62762/JAIB.2026.698089},
  url = {https://www.icck.org/article/abs/JAIB.2026.698089},
  abstract = {The integration of artificial intelligence into biomedical image analysis represents a paradigm shift in clinical decision support and surgical informatics. Laparoscopic cholecystectomy (LC) is a widely performed minimally invasive procedure but remains technically challenging due to limited visibility, anatomical variations, and the risk of complications such as bile duct injury. Artificial intelligence (AI), particularly deep learning (DL), has shown great potential for improving intraoperative understanding through automated surgical video analysis. This review summarizes recent advances in DL-based LC video analysis, focusing on semantic segmentation and surgical action or phase recognition. Existing approaches are categorized into convolutional neural networks (CNNs), transformer-based architectures, and hybrid spatio-temporal models. For segmentation, U-Net variants, DeepLab architectures, and vision transformers are reviewed for identifying anatomical structures and surgical instruments. For action and phase recognition, CNN-LSTM frameworks, 3D CNNs, and video transformers are discussed for modeling surgical workflows and temporal dependencies. We also summarize commonly used datasets, including Cholec80 and CholecSeg8k, and evaluation metrics such as Dice score, IoU, accuracy, and F1-score. Major challenges include data scarcity, inter-surgeon variability, domain shift, and real-time computational constraints. Future research should emphasize self-supervised learning, multi-modal fusion, and clinically explainable AI to improve robustness, generalizability, and real-world surgical deployment.},
  keywords = {laparoscopic cholecystectomy, deep learning, semantic segmentation, surgical phase recognition, surgical action recognition},
  issn = {3068-7535},
  publisher = {Institute of Central Computation and Knowledge}
}