Amod Jog, PhD (Martinos Center for Biomedical Imaging)
Seminar Title: Machine Learning for Medical Imaging
Date: June 8
Time: 4pm – 5pm
Accurate automatic segmentation of brain anatomy from T1-weighted magnetic resonance images (MRI) has been a computationally intensive bottleneck in neuroimaging pipelines, with state-of-the-art results obtained by unsupervised intensity modeling-based methods and multi-atlas registration and label fusion. With the advent of powerful supervised convolutional neural networks (CNN)-based learning algorithms, it is now possible to produce a high quality brain segmentation within seconds. However, the very supervised nature of these methods makes it difficult to generalize them on data different from what they have been trained on. Modern neuroimaging studies are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is not possible to standardize the whole gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this talk, I will describe a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input T1-weighted acquisition. Our approach relies on building approximate forward models of T1-weighted pulse sequences that produce a typical test image. We use the forward models to augment the training data with test data specific training examples. These augmented data can be used to update and/or build a more robust segmentation model that is more attuned to the test data imaging properties. Our method generates highly accurate, state-of-the-art segmentation results, within seconds and is consistent across a wide-range of protocols.
Amod Jog is a Research Fellow at the Laboratory of Computational Neuroimagingat the MGH/HST Athinoula A. Martinos Center for Biomedical Imaging.He earned his PhD in Computer Science at the Johns Hopkins University in 2016. His research interests are in medical image analysis, specifically in image segmentation, registration, and synthesis.