Papers
4
Total Citations
63
H-Index
4
About
Abdolrahim Kadkhodamohammadi’s research lies at the intersection of computer vision and computer-assisted interventions, with a focus on enabling intelligent systems for minimally invasive surgery. His key contributions include developing methods for articulated clinician detection using 3D pictorial structures on RGB-D data (31 citations), which addressed the challenge of tracking clinicians in complex surgical environments. He also advanced real-time multiple surgical tool tracking (18 citations), a critical building block for applications like video summarization, workflow analysis, and surgical navigation, overcoming challenges such as fast instrument motion in laparoscopic data. Additionally, he contributed to the design of the “Endoscopic Vision Challenge” for MICCAI 2020 (8 citations), a notable achievement that helped standardize evaluation in the field. His work on feature aggregation decoders for segmenting laparoscopic scenes (6 citations) further demonstrates his commitment to improving scene understanding in surgery. With a total of over 60 citations, Kadkhodamohammadi’s research has had a tangible impact on the development of real-time, vision-based tools that enhance surgical precision and workflow efficiency.
Research Focus
Key Achievements
Top Papers
- 1Articulated clinician detection using 3D pictorial structures on RGB-D data31 citations · 2016
- 2Towards real-time multiple surgical tool tracking18 citations · 2020
- 3Endoscopic Vision Challenge8 citations · 2020
- 4Feature Aggregation Decoder for Segmenting Laparoscopic Scenes6 citations · 2019