# Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy

arXiv: https://arxiv.org/abs/2007.02361 · HTML: https://mlsanity.com/paper/2007.02361/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images](https://mlsanity.com/paper/2103.03423/) (2021)
- [Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images](https://mlsanity.com/paper/2109.01303/) (2021)
- [Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification](https://mlsanity.com/paper/2103.03629/) (2021)
- [Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery](https://mlsanity.com/paper/2110.03912/) (2021)
- [Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery](https://mlsanity.com/paper/2307.03662/) (2023)
- [Self-supervised Tumor Segmentation through Layer Decomposition](https://mlsanity.com/paper/2109.03230/) (2021)
- [3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training](https://mlsanity.com/paper/2106.05525/) (2021)
- [MixCL: Pixel label matters to contrastive learning](https://mlsanity.com/paper/2203.02114/) (2022)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)

## Abstract (arXiv)

Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging conditions (e.g., low texture, overexposure, etc.), automatic semantic segmentation is a challenging scenario, which justifies the scarce literature on this topic. In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy. To further regularise the depth estimation, we propose the use of clean training images captured by the stereo arthroscope of routine objects (presenting none of the poor imaging conditions and with rich texture information) to pre-train the model. We fine-tune such model to produce both the semantic segmentation and self-supervised monocular depth using stereo arthroscopic images taken from inside the knee. Using a data set containing 3868 arthroscopic images captured during cadaveric knee arthroscopy with semantic segmentation annotations, 2000 stereo image pairs of cadaveric knee arthroscopy, and 2150 stereo image pairs of routine objects, we show that our semantic segmentation regularised by self-supervised depth estimation produces a more accurate segmentation than a state-of-the-art semantic segmentation approach modeled exclusively with semantic segmentation annotation.