Papers › Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Michael A. Riegler, Dag Johansen, Håvard D. Johansen, Pål Halvorsen
Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have demonstrated potential for computer-assisted procedures. However, there exists challenges and requirements to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can be explored for the automated segmentation of surgical instruments in laparoscopy, an important step towards tool tracking. Our experimental results exhibit that the Dual decoder attention network (DDANet) produces a superior result compared to other recent deep learning methods. DDANet yields a Dice coefficient of 0.8739 and mean intersection-over-union of 0.8183 for the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at a real-time speed of 101.36 frames-per-second that is critical for such procedures.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Image Segmentation | ROBUST-MIS | ColonsegNet | DSC | 0.8495 | #1 of 4 | Archive leaderboard | report |
| Medical Image Segmentation | ROBUST-MIS | ColonsegNet | FPS | 185.54 | #1 of 4 | Archive leaderboard | report |
| Medical Image Segmentation | ROBUST-MIS | ColonsegNet | mIoU | 0.7943 | #1 of 4 | Archive leaderboard | report |
| Medical Image Segmentation | ROBUST-MIS | DDANet | DSC | 0.8739 | #2 of 4 | Archive leaderboard | report |
| Medical Image Segmentation | ROBUST-MIS | DDANet | FPS | 101.36 | #2 of 4 | Archive leaderboard | report |
| Medical Image Segmentation | ROBUST-MIS | DDANet | mIoU | 0.8183 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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