Deep Learning, Image Processing, Pattern Tecognition, GPU Parallelism, Cloud-based Computing

Deep Learning & OCT

Intogral Limited has developed state-of-the-art segmentation tools built with novel Deep Learning architectures bespoke for OCT imaging. See the training process for Macular Holes (how AI learns to segment the output over time) in the video below. The bottom two rows show the AI output where the final result is at the end of the video:

And see how our AI approach learns to segment the Macular Edema:

And see the final AI solution deployed on the cloud:

Medical Image Analysis

Intogral Limited harnesses cutting-edge research into GPU image processing algorithms, enabling real-time solutions with unprecedented accuracy to problems that otherwise can be only poorly approximated and/or require huge computational time. With many of our customers relying heavily on automation and high-throughput, both speed and accuracy are essential.

Moreover, Intogral Limited maintains strong links with Durham University’s Image Informatics research group allowing Intogral Limited to maintain a close connection to current and future state-of-the-art research and cutting-edge solutions.

Selected Publications:

1. Chris G. Willcocks, P. T. Jackson, C. J. Nelson, A. Nasrulloh, and B. Obara, “Interactive GPU Active Contours for Segmenting Inhomogeneous Objects,” Journal of Real-time Image Processing, Dec. 2017, ISSN: 1861-8200, DOI: 10.1007/s11554-017-0740-1

2. Chris G. Willcocks, P. T. Jackson, C. J. Nelson, and B. Obara, “Extracting 3D Parametric Curves from 2D Images of Helical Objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 9, PP. 1757–1769, Sep. 2017, ISSN: 0162-8828. DOI: 10.1109/TPAMI.2016.2613866.

3. S. Ackay, M. Kundegorski, Chris G. Willcocks, and T. P. Breckon, “On Using Deep Convolutional Neural Network Architectures for Automated Object Detection and Classification within X-ray Baggage Security Imagery,” IEEE Transactions on Information Forensics and Security, Oct. 2017

4. A. V. Nasrulloh, C. G. Willcocks, P. T. G. Jackson, C. Geenen, M. S. Habib, D. H. W. Steel, and B. Obara, “Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes,” IEEE Transactions on Medical Imaging, vol. PP, no. 99, PP. 1–1, 2017, ISSN: 0278-0062. DOI: 10.1109/TMI.2017.2767908