STIR Challenge

STIR Challenge

MICCAI 2026

STIR Challenge 2026

MICCAI 2026 Logo

Description

The 2026 STIR (Surgical Tattoos in Infrared) Challenge (STIRC2026) is a challenge to quantify methods in deformable tracking, mapping and reconstruction. STIR is a part of EndoVis challenge at MICCAI 2026. STIR will be quantified using a non-public test split of the publicly available STIR Dataset, STIROrig. The dataset is a stereo dataset with ground truth labels created using Infrared Tattoos1.

Objective

Track points accurately and efficiently in videos.

Motivation

Robust tracking and mapping in deformable scenarios is essential to enable downstream tasks in medical computer vision, such as motion compensation, and subtask automation2. A robust means to evaluate these methods for clinical efficacy requires a large labelled dataset, and a standardized evaluation. The STIR challenge provides that.

Example with MFTs3

Here are some examples of a baseline method (MFTs) on the STIR dataset.

News

  • May 21, 2026 Webpage updated for STIRC 2026

Timeline

STIR 2026 Timeline

  • May 30, 2026 Open for submissions
  • September 04, 2026 Submissions deadline
  • September 06, 2026 Submission reports deadline
  • September 27 - October 01, 2026 Challenge day at MICCAI 2026

Organizers

NameInstitution
Adam SchmidtIntuitive
Mert KaraogluImFusion & TUM
Alexander LadikosImFusion
Omid MohareriIntuitive
Simon DiMaioIntuitive

Sponsors

Sponsors: ImFusion, Intuitive

Contact

Questions about our challenge should be posted on the synapse forum for this challenge. To formally register for the challenge, please email your signed registration form to challenge.stir@gmail.com.

References

  1. Schmidt, Adam, Omid Mohareri, Simon DiMaio, and Septimiu E. Salcudean. “STIR: Surgical Tattoos in Infrared.” IEEE Transactions on Medical Imaging (2024).
  2. Schmidt, Adam, Omid Mohareri, Simon DiMaio, Michael C. Yip, Septimiu E. Salcudean. “Tracking and mapping in medical computer vision: A review.” Medical Image Analysis. 2024 Mar 2:103131.
  3. Neoral, Michal, Jonáš Šerých, and Jiří Matas. “Mft: Long-term tracking of every pixel.” In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6837-6847. 2024.