Portrait of Antonio Criminisi

Antonio Criminisi

Principal Researcher


Hi, I am a Principal Researcher at Microsoft, working on artificial intelligence, machine learning, computer vision and medical image analysis.

AI for Cancer Treatment

I am particularly interested in deploying artificial intelligence systems that work alongside doctors, and help them deliver more effective healthcare for their cancer patients. Please check this 5-minute video, and the project InnerEye for more information on how we use machine learning in cancer treatment.


The paper Deep Neural Decision Forests that I have co-authored with P. Kontschieder, M. Fiterau and S. Rota Bulo’ has been awarded the prestigious David Marr Prize at the ICCV 2015 conference in Chile.

Please check the Decision Forests project if you are interested in the theory and application of forests in computer vision and medical image analysis.

I have co-authored a large number of scientific papers and books, check my “publications” tab if interested.


Here is an interview for the BBC on the InnerEye research project.



SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips

Established: June 29, 2015

We present a new interactive approach to 3D scene understanding. Our system, SemanticPaint, allows users to simultaneously scan their environment, whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the environment. Unlike offline systems, where capture, labeling and batch learning often takes hours or even days to perform, our approach is fully online. To be…

Learning to be a depth camera for close-range human capture and interaction

Established: July 14, 2014

We present a machine learning technique for estimating absolute, per-pixel depth using any conventional monocular 2D camera, with minor hardware modifications. Our approach targets close-range human capture and interaction where dense 3D estimation of hands and faces is desired. We use hybrid classification-regression forests to learn how to map from near infrared intensity images to absolute, metric depth in real-time. We demonstrate a variety of human computer interaction scenarios.  

Filter Forests for Learning Data-Dependent Convolutional Kernels

Established: February 10, 2014

We propose ‘filter forests’ (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filtering, where it attempts to learn the optimal filtering kernels to be applied to each data point. The model can learn both the size of the kernel and its values, conditioned on the observation and its spatial or temporal context.…

RGB-D Dataset 7-Scenes

Established: January 1, 2013

The 7-Scenes dataset is a collection of tracked RGB-D camera frames. The dataset may be used for evaluation of methods for different applications such as dense tracking and mapping and relocalization techniques. Overview All scenes were recorded from a handheld Kinect RGB-D camera at 640x480 resolution. We use an implementation of the KinectFusion system to obtain the 'ground truth' camera tracks, and a dense 3D model. Several sequences were recorded per scene by different users,…

Decision Forests

Established: July 25, 2012

Decision Forests for Computer Vision and Medical Image Analysis A. Criminisi and J. Shotton Springer 2013, XIX, 368 p. 143 illus., 136 in color. ISBN 978-1-4471-4929-3  

Touchless Interaction in Medical Imaging

Established: November 16, 2011

This project explores the use of new touchless technology in medical practice. With advances in medical imaging over the years, surgical procedures have become increasingly reliant on a range of digital imaging systems for navigation, reference, diagnosis and documentation. The need to interact with images in these surgical settings offers particular challenges arising from the need to maintain boundaries between sterile and non-sterile aspects of the surgical environment and practices. Traditional input devices such as…

Human Pose Estimation for Kinect

Established: January 25, 2011

Kinect for Xbox 360 and Windows makes you the controller by fusing 3D imaging hardware with markerless human-motion capture software. Our group investigates such software. Mixing computer vision, graphics, and machine learning techniques, we look at how to build algorithms that can learn to recognize human poses quickly and reliably. Images Traditional RGB image Image from new depth sensing camera Body parts inferred by our recognition algorithm 3D body part position proposals Related Press Binary…

InnerEye – Medical Imaging AI to Empower Clinicians

Established: October 7, 2008

InnerEye is a research project that uses state of the art artificial intelligence to build innovative image analysis tools to help doctors treat diseases such as cancer in a more targeted and effective way. "...We are pursuing AI so that we can empower every person and every institution that people build with tools of AI so that they can go on to solve the most pressing problems of our society and our economy. That’s the…

Image and Video Editing at MSR Cambridge

Established: January 23, 2002

At Microsoft Research in Cambridge we are developing new machine vision algorithms for intelligent image and video editing and browsing. Our technology provides tools for: accurate interactive segmentation and matting, color correction, easy object removal and image restoration, and seamless object insertion. News! AutoCollage is now available as a product from Microsoft Research Cambridge. Click here to get a free trial version.  Computer Vision at MSR Cambridge

Image Understanding

Established: January 1, 2000

At Microsoft Research in Cambridge we are developing new machine vision algorithms for automatic recognition and segmentation of many different object categories. We are interested in both the supervised and unsupervised scenarios.   Research data Download labelled image databases for supervised learning in the "Downloads" link below. The data provided here may be used freely for research purposes but it cannot be used for commercial purposes. Database of thousands of weakly labelled, high-res images. Pixel-wise labelled…






Infrared depth sensor based automated classification of motor dysfunction in multiple sclerosis – a proof-of-concept study
Marcus DSouza, J. Burggraaf, Christian P. Kamm, Prejass Tewarie, Peter Kontschieder, Jonas F. Dorn, Cecily Morrison, Thomas Vogel, Abigail Sellen, Matthias Machacek, Peter Chin, Antonio Criminisi, Frank Dahlke, Bernard Uitdehaag, Ludwig Kappos, in European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), September 1, 2014, View abstract, View external link
Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos
Peter Kontschieder, Jonas F. Dorn, Cecily Morrison, Bob Corish, Darko Zikic, Abigail Sellen, Marcus DSouza, Christian P. Kamm, Jessica Burggraaff, Prejaas Tewarie, Thomas Vogel, Michael Azzarito, Ben Glocker, Peter Chin, Frank Dahlke, Chris Polman, Ludwig Kappos, Bernard Uitdehaag, Antonio Criminisi, in MICCAI 2014 - Intl Conf. on Medical Image Computing and Computer Assisted Intervention, Springer, September 1, 2014, View abstract, Download PDF




















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December 2, 2008


Sherwood C++ and C# code library for decision forests.

December 2012

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Microsoft Research Volume Rendering SDK

May 2012

COM components which provides scalable implementation of real-time volume rendering intended for server-based GPUs. It could also be accessed from .Net clients using the provided Runtime Callable Wrapper (RCW)

Size: 179 MB

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Geodesic High-Dynamic-Range Photography Tool

April 2012

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