Arterys Inc.

Sean Patrick Sall

Daniel Irving Golden

Jesse Lieman-Sifry

Hok Kan Lau

John Axerio-Cilies

Matthew Joseph Didonato

Torin Arni Taerum

Matthieu Le

Robert George Newton

Carla Leibowitz

Fabien Rafael David Beckers

Shek Bun Law

Anitha Priya Krishnan

Angélique Sophie Calmon

Berk Dell Norman

Martin Simonovsky

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Content based image retrieval for lesion analysis

Systems and methods for providing improved eddy current correction (ECC) in medical imaging environments. One or more of the embodiments disclosed herein provide a deep learning-based convolutional neural network (CNN) model trained to automatically generate an ECC mask which may be composited with two-dimensional (2D) scan slices or four-dimensional (4D) scan slices and made viewable through, for example, a web application, and made manipulable through a user interface thereof.

Deep learning-based eddy current correction

Systems and methods for providing a novel framework to simulate the appearance of pathology on patients who otherwise lack that pathology. The systems and methods include a 'simulator' that is a generative adversarial network (GAN). Rather than generating images from scratch, the systems and methods discussed herein simulate the addition of diseases-like appearance on existing scans of healthy patients. Focusing on simulating added abnormalities, as opposed to simulating an entire image, significantly reduces the difficulty of training GANs and produces results that more closely resemble actual, unmodified images. In at least some implementations, multiple GANs are used to simulate pathological tissues on scans of healthy patients to artificially increase the amount of available scans with abnormalities to address the issue of data imbalance with rare pathologies.

Simulating abnormalities in medical images with generative adversarial networks

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Indianapolis, IN, United States of America

Sean Patrick Sall