# OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images

arXiv: https://arxiv.org/abs/2004.12668 · HTML: https://mlsanity.com/paper/2004.12668/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion](https://mlsanity.com/paper/2111.04911/) (2021)
- [How can we learn (more) from challenges? A statistical approach to driving future algorithm development](https://mlsanity.com/paper/2106.09302/) (2021)
- [SegMatch: A semi-supervised learning method for surgical instrument segmentation](https://mlsanity.com/paper/2308.05232/) (2023)
- [Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy](https://mlsanity.com/paper/2107.02319/) (2021)
- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)
- [Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet](https://mlsanity.com/paper/2103.09289/) (2021)
- [Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy](https://mlsanity.com/paper/2104.01268/) (2021)
- [Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask](https://mlsanity.com/paper/2203.11358/) (2022)
- [Label-supervised surgical instrument segmentation using temporal equivariance and semantic continuity](https://mlsanity.com/paper/2403.09551/) (2024)
- [Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation](https://mlsanity.com/paper/2607.08397/) (2026)

## Abstract (arXiv)

Segmentation of endoscopic images is an essential processing step for computer and robotics-assisted interventions. The Robust-MIS challenge provides the largest dataset of annotated endoscopic images to date, with 5983 manually annotated images. Here we describe OR-UNet, our optimized robust residual 2D U-Net for endoscopic image segmentation. As the name implies, the network makes use of residual connections in the encoder. It is trained with the sum of Dice and cross-entropy loss and deep supervision. During training, extensive data augmentation is used to increase the robustness. In an 8-fold cross-validation on the training images, our model achieved a mean (median) Dice score of 87.41 (94.35). We use the eight models from the cross-validation as an ensemble on the test set.