TY - GEN
T1 - Enhancing Surgical Precision
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
AU - Rokaya, Akter
AU - Islam, Shuvo Md Touhidul
AU - Mostafa, Kazi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Minimally Invasive Surgery (MIS) has revolutionised surgical procedures, offering patients less invasive and more efficient treatments. However, MIS presents limited view and depth perception challenges, impacting surgical accuracy and safety. This study focuses on advancing depth estimation in MIS, exploring a range of methodologies to enhance precision and efficiency. We conduct an exhaustive review of contemporary approaches, encompassing conventional methods like stereo matching and structure from motion alongside cutting-edge deep learning techniques. We address specific challenges MIS poses, including issues related to low image quality and the non-rigid nature of tissues. We introduce an innovative deep learning-based framework, leveraging the MiDaS model for depth estimation of endoscopic images. This framework employs convolutional neural networks (CNN) to map input images to their corresponding depth maps. In conclusion, we envision a multitude of potential applications and future directions for depth estimation within MIS, emphasising its potential to enhance surgical precision and safety.
AB - Minimally Invasive Surgery (MIS) has revolutionised surgical procedures, offering patients less invasive and more efficient treatments. However, MIS presents limited view and depth perception challenges, impacting surgical accuracy and safety. This study focuses on advancing depth estimation in MIS, exploring a range of methodologies to enhance precision and efficiency. We conduct an exhaustive review of contemporary approaches, encompassing conventional methods like stereo matching and structure from motion alongside cutting-edge deep learning techniques. We address specific challenges MIS poses, including issues related to low image quality and the non-rigid nature of tissues. We introduce an innovative deep learning-based framework, leveraging the MiDaS model for depth estimation of endoscopic images. This framework employs convolutional neural networks (CNN) to map input images to their corresponding depth maps. In conclusion, we envision a multitude of potential applications and future directions for depth estimation within MIS, emphasising its potential to enhance surgical precision and safety.
KW - Convolutional Neural Networks
KW - Depth Estimation
KW - Endoscopic Imaging
KW - MiDaS Model
KW - Minimally Invasive Surgery
UR - http://www.scopus.com/inward/record.url?scp=85211327850&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70687-5_5
DO - 10.1007/978-3-031-70687-5_5
M3 - Conference Proceeding
AN - SCOPUS:85211327850
SN - 9783031706868
T3 - Lecture Notes in Networks and Systems
SP - 46
EP - 57
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P. P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 December 2023 through 8 December 2023
ER -