Mask-guided Data Augmentation for Multiparametric MRI Generation with a
  Rare Hepatocellular Carcinoma
Karen Sanchez Carlos Hinojosa Kevin Arias Henry Arguello Denis Kouame Olivier Meyrignac Adrian Basarab
Data augmentation is classically used to improve the overall performance of
deep learning models. It is, however, challenging in the case of medical
applications, and in particular for multiparametric datasets. For example,
traditional geometric transformations used in several applications to generate
synthetic images can modify in a non-realistic manner the patients' anatomy.
Therefore, dedicated image generation techniques are necessary in the medical
field to, for example, mimic a given pathology realistically. This paper
introduces a new data augmentation architecture that generates synthetic
multiparametric (T1 arterial, T1 portal, and T2) magnetic resonance images
(MRI) of massive macrotrabecular subtype hepatocellular carcinoma with their
corresponding tumor masks through a generative deep learning approach. The
proposed architecture creates liver tumor masks and abdominal edges used as
input in a Pix2Pix network for synthetic data creation. The method's efficiency
is demonstrated by training it on a limited multiparametric dataset of MRI
triplets from $89$ patients with liver lesions to generate $1,000$ synthetic
triplets and their corresponding liver tumor masks. The resulting Frechet
Inception Distance score was $86.55$. The proposed approach was among the
winners of the 2021 data augmentation challenge organized by the French Society
of Radiology.