# Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures

arXiv: https://arxiv.org/abs/2103.15997 · HTML: https://mlsanity.com/paper/2103.15997/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [YOLACT++: Better Real-time Instance Segmentation](https://mlsanity.com/paper/1912.06218/) (2019)
- [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](https://mlsanity.com/paper/1809.04430/) (2018)
- [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019)
- [Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room](https://mlsanity.com/paper/2005.03501/) (2020)
- [Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery](https://mlsanity.com/paper/1805.02475/) (2018)

## Built on by (most cited first, 6 of 6)

- [Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art](https://mlsanity.com/paper/2304.13014/) (2023)
- [Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging](https://mlsanity.com/paper/2210.15033/) (2022)
- [On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification](https://mlsanity.com/paper/2205.00895/) (2022)
- [Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy](https://mlsanity.com/paper/2107.02319/) (2021)
- [Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask](https://mlsanity.com/paper/2203.11358/) (2022)
- [Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams](https://mlsanity.com/paper/2206.07580/) (2022)

## Abstract (arXiv)

Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and annotated datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time (5 frames-per-second (fps)at most). However, in order for the method to be clinically applicable, real-time capability is utmost required along with high accuracy. In this paper, we propose the addition of attention mechanisms to the YOLACT architecture that allows real-time instance segmentation of instrument with improved accuracy on the ROBUST-MIS dataset. Our proposed approach achieves competitive performance compared to the winner ofthe 2019 ROBUST-MIS challenge in terms of robustness scores,obtaining 0.313 MI_DSC and 0.338 MI_NSD, while achieving real-time performance (37 fps)