Surgical Data Science

Surgical data science and team mission

Surgical Data Science  (SDS) is about improving outcomes of surgery with AI-based software systems driven by clinical data.  We aim to improve the training and capabilities of surgical teams with SDS, to reduce complications, make surgery easier and safer, and achieve better patient outcomes. 

Disrumpere

Ultrasound (US) is a key technology to detect abdominal cancer early, and treat it with minimal intervention. The disrumpere project aims to combine low-cost ultrasound devices with innovative AI and robotics technologies, to make US easier, faster more widely used.  

Laparoscopic surgical guidance systems with Augmented Reality

We research computer systems to improve laparoscopic surgery with Augmented Reality (AR) technologies. 3D medical image data such as CT or MR is automatically combined with the laparoscopic video, to show hidden critical structures such as tumours and major vessels.

Percutaneous surgical guidance systems

We research computer systems to improve percutaneous surgery with Virtual Reality (VR) and 3D tracking technologies. 

Ultrasound and flexible endoscopy educational systems

Objective skill assessment is becoming an increasingly important component of surgery education and high-stakes skill assessment for accreditation. Our goal is to combine low-cost mechanical simulators with AI to make these tools broadly accessible.

Software

Sight

Sight, the Surgical Image Guidance and Healthcare Toolkit facilitates the creation of software based on medical imaging.

It includes various features such as 2D and 3D medical image processing (CT/MRI/US), video processing, visualization, augmented reality, and connectivity with tracking systems. It can be used to write navigation systems, simulators, planning software, or even simple video filtering applications.

Sight is written in C++ and built on top of the best open-source libraries in the field such as OpenCV, ITK, VTK, PCL, and Qt and makes their usage easier by providing data common formats and wrappers. It is based on a modular object/service architecture, making building software application as simple as connecting together data, algorithms and user interface. It runs on Windows and Linux and is freely available under the LGPL.

Visualisation

  • Multi-Planar Reconstruction
  • Direct volume rendering
  • Mixed Rendering (Volume + Surfaces)
  • OpenGL 4.x programmable  pipeline
  • Fast rendering pipeline

Formats and protocols

  • DICOM CT/MRI images
  • SCP/SCU
  • OpenIGTLink
  • Video files: MP4/AVI/MKV/…
  • Cameras: Intel Realsense, most USB webcams.
  • RTP/RTSP streaming

Fast prototyping

  • Interface and application design with XML or QML files
  • Reusable algorithms and widgets as services
  • Modular code, dynamic library loading
  • Easy application packaging
  • Prebuilt binaries for 3rd part libraries

Augmented reality

  • Camera calibration
  • Lens distortion and undistortion compensation
  • Virtual 2D/3D scene superimposition onto video
  • Precise synchronisation of the video and the virtual layer
  • Optical tracking with Aruco tags

Software applications​

SightViewer

Medical image and segmentation viewer. It supports many popular formats including DICOM and VTK.

SightCalibrator

User-friendly application to calibrate mono and stereo cameras. Very handy since camera calibration is a prerequisite in any AR application.

Clinical and technical publications

Mixed reality navigation system for ultrasound-guided percutaneous punctures: a pre-clinical evaluation

Artificial intelligence and surgery: Recent progress and future perspectives

Automatic task recognition in a flexible endoscopy benchtop trainer with semi-supervised learning

Light modelling and calibration in laparoscopy

Datasets

Liver segmentation

3D-IRCADb-01

This dataset is composed of the CT-scans of 10 women and 10 men with hepatic tumors in 75% of cases.

Where appropriate, the Couinaud segment number corresponding to the location of tumors is also provided.

Respiratory cycle

3D-IRCADb-02

This dataset is composed of 2 anonymized CT-scans.

The first one has been realized during the arterial phase in inhaled position, whereas the second one has been realized during the portal phase in exhaled position.

The patient has a hepatic focal nodular hyperplasia in segment VII according to Couinaud’s description.

The DEPOLL dataset for evaluating registration accuracy in AR-guided liver surgery

DePoLL (the Deformable Porcine Laparoscopic Liver) dataset was created to quantitatively evaluate registration accuracy for AR-guided liver surgery using a pre-operative CT model.

Crew

Dr. Alexandre Hostettler

Head of Surgical Data Science

Sneakers addict

Dr. Flavien BRIDAULT

Director of Software Development

Computer graphics, software engineering, agile methodology, mindfulness, vegetables addict

Dr. Toby COLLINS

Director of Research

Research communication, Machine learning, Computer vision, Medical image analysis, Project management, The English Guy

Josiane Uwineza

Research engineer

Python, Data science, Machine learning, Deep learning, Computer vision, prayer

Dr. Alexandre ANCEL

Research engineer

Software engineering, Surgical navigation systems, Medical image analysis, Computer graphics, Deep-Learning, Emacs evangelist

Marc SCHWEITZER

Senior software developer

Computer Vision, C++, CMake developer, bicycle commuter

Mathieu Haller

Research engineer

Data science, Deep learning, Python, C++, Scotland-lover

Didier WECKMANN

Senior software developer

C++, Python, Javascript, Software architecture, Continuous integration, science fanboy

Dr. Patrick Niyishaka

Research engineer

Python, data science, machine learning deep learning, computer vision, digital image processing, basketball and travel lover

Richard Nkusi

Research engineer

Data science, Machine learning, Deep learning, Computer vision, Believer

Yvonne Keeza

Medical Imaging Annotator

Medical image data analysis, radiology, ultrasound, project management, image protocol and annotations, medical technology enthusiast, salsa dancer

Erwan Duhamel

Phd. Student

C++, Computer graphics, Volume rendering, Vulkan pilgrim

Grace Ufitinema

Medical Imaging Annotator

Medical image analysis, radiography, ultrasound, annotation, basketball-lover

Florien Ujemurwego

Medical Imaging Annotator

Medical image analysis, radiography, ultrasound,
annotation, medical image management, swimming-lover

Bereket Frezgiy

Research engineer

Machine learning, computer vision, Medical image analysis, deep learning and Research communication, cycling race world tours

Güinther Saibro

Phd. Student

Python, Deep learning, Statistics, Medical image analysis, Ultrasound, Cycling 

Lucas Schmidt

Master Student

Software engineering, C++, Testing, Continuous Integration, KDE lover

William Ndzimbong

Phd. Student

Python, Matlab, deep learning, computer vision, numerical optimization, new culture hunter

Dr. Michael KUGLER

Research engineer

Mechanics, C++, Numerical optimization, Software engineering, Research communication

Careers

IRCAD Africa

C++ developer

  • Develop new surgical navigation systems integrating our latest advances in AI.
  • Develop new features and provide support on our open-source toolkit Sight.

Partners