3 research outputs found
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
Understanding the intensity characteristics of brain lesions is key for
defining image-based biomarkers in neurological studies and for predicting
disease burden and outcome. In this work, we present a novel foreground-based
generative method for modelling the local lesion characteristics that can both
generate synthetic lesions on healthy images and synthesize subject-specific
pseudo-healthy images from pathological images. Furthermore, the proposed
method can be used as a data augmentation module to generate synthetic images
for training brain image segmentation networks. Experiments on multiple
sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI)
demonstrate that the proposed method can generate highly realistic
pseudo-healthy and pseudo-pathological brain images. Data augmentation using
the synthetic images improves the brain image segmentation performance compared
to traditional data augmentation methods as well as a recent lesion-aware data
augmentation technique, CarveMix. The code will be released at
https://github.com/dogabasaran/lesion-synthesis.Comment: 13 pages, 6 figures, 2022 MICCAI SASHIMI (Simulation and Synthesis in
Medical Imaging) Workshop pape
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Data augmentation has become a de facto component of deep learning-based
medical image segmentation methods. Most data augmentation techniques used in
medical imaging focus on spatial and intensity transformations to improve the
diversity of training images. They are often designed at the image level,
augmenting the full image, and do not pay attention to specific abnormalities
within the image. Here, we present LesionMix, a novel and simple lesion-aware
data augmentation method. It performs augmentation at the lesion level,
increasing the diversity of lesion shape, location, intensity and load
distribution, and allowing both lesion populating and inpainting. Experiments
on different modalities and different lesion datasets, including four brain MR
lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix
achieves promising performance in lesion image segmentation, outperforming
several recent Mix-based data augmentation methods. The code will be released
at https://github.com/dogabasaran/lesionmix.Comment: 13 pages, 5 figures, 4 tables, MICCAI DALI Workshop 202
Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
Cardiovascular disease, the leading cause of death globally, is an
age-related disease. Understanding the morphological and functional changes of
the heart during ageing is a key scientific question, the answer to which will
help us define important risk factors of cardiovascular disease and monitor
disease progression. In this work, we propose a novel conditional generative
model to describe the changes of 3D anatomy of the heart during ageing. The
proposed model is flexible and allows integration of multiple clinical factors
(e.g. age, gender) into the generating process. We train the model on a
large-scale cross-sectional dataset of cardiac anatomies and evaluate on both
cross-sectional and longitudinal datasets. The model demonstrates excellent
performance in predicting the longitudinal evolution of the ageing heart and
modelling its data distribution. The codes are available at
https://github.com/MengyunQ/AgeHeart