# Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask

arXiv: https://arxiv.org/abs/2203.11358 · HTML: https://mlsanity.com/paper/2203.11358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [2017 Robotic Instrument Segmentation Challenge](https://mlsanity.com/paper/1902.06426/) (2019)
- [Learning Open-World Object Proposals without Learning to Classify](https://mlsanity.com/paper/2108.06753/) (2021)
- [YOLACT++: Better Real-time Instance Segmentation](https://mlsanity.com/paper/1912.06218/) (2019)
- [Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning](https://mlsanity.com/paper/1803.01207/) (2018)
- [Learning to Segment Object Candidates](https://mlsanity.com/paper/1506.06204/) (2015)
- [Learning to Refine Object Segments](https://mlsanity.com/paper/1603.08695/) (2016)
- [ISINet: An Instance-Based Approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2007.05533/) (2020)
- [Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room](https://mlsanity.com/paper/2005.03501/) (2020)
- [What makes for effective detection proposals?](https://mlsanity.com/paper/1502.05082/) (2015)
- [ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools](https://mlsanity.com/paper/1706.08126/) (2017)
- [Concurrent Segmentation and Localization for Tracking of Surgical Instruments](https://mlsanity.com/paper/1703.10701/) (2017)
- [Deep Residual Learning for Instrument Segmentation in Robotic Surgery](https://mlsanity.com/paper/1703.08580/) (2017)
- [4D Generic Video Object Proposals](https://mlsanity.com/paper/1901.09260/) (2019)
- [FastMask: Segment Multi-scale Object Candidates in One Shot](https://mlsanity.com/paper/1612.08843/) (2016)
- [OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images](https://mlsanity.com/paper/2004.12668/) (2020)
- [AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects](https://mlsanity.com/paper/1811.08728/) (2018)
- [Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures](https://mlsanity.com/paper/2103.15997/) (2021)
- [Localizing Small Apples in Complex Apple Orchard Environments](https://mlsanity.com/paper/2202.11372/) (2022)
- [Superpixel-based Refinement for Object Proposal Generation](https://mlsanity.com/paper/2101.04574/) (2021)
- [DeepFH Segmentations for Superpixel-based Object Proposal Refinement](https://mlsanity.com/paper/2108.03503/) (2021)

## Abstract (arXiv)

Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in medical image processing. However, image degradations, small instruments, and the generalization between different surgery types make medical instrument segmentation challenging. To cope with these challenges, we adapt the object proposal generation system AttentionMask and propose a dedicated post-processing to select promising proposals. The results on the ROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is a strong foundation for generating state-of-the-art performance. Our evaluation in an object proposal generation framework shows that our adapted AttentionMask system is robust to image degradations, generalizes well to unseen types of surgeries, and copes well with small instruments.