MRI Scan Synthesis Methods based on Clustering and Pix2Pix

TL;DR

This paper explores methods to synthesize missing MRI scans, particularly T2-weighted images from T1-weighted scans, using clustering and Pix2Pix, to improve segmentation accuracy when scans are unavailable or blurry.

Contribution

It introduces a clustering-based and a Pix2Pix GAN approach for MRI scan synthesis, demonstrating their effectiveness in maintaining segmentation quality.

Findings

Clustering-based method achieved lowest mean squared error.

Pix2Pix method provided good segmentation results with synthesized scans.

Synthesized scans enabled effective tumor segmentation in many cases.

Abstract

We consider a missing data problem in the context of automatic segmentation methods for Magnetic Resonance Imaging (MRI) brain scans. Usually, automated MRI scan segmentation is based on multiple scans (e.g., T1-weighted, T2-weighted, T1CE, FLAIR). However, quite often a scan is blurry, missing or otherwise unusable. We investigate the question whether a missing scan can be synthesized. We exemplify that this is in principle possible by synthesizing a T2-weighted scan from a given T1-weighted scan. Our first aim is to compute a picture that resembles the missing scan closely, measured by average mean squared error (MSE). We develop/use several methods for this, including a random baseline approach, a clustering-based method and pixel-to-pixel translation method by Isola et al. (Pix2Pix) which is based on conditional GANs. The lowest MSE is achieved by our clustering-based method. Our second aim is to compare the methods with respect to the effect that using the synthesized scan has on the segmentation process. For this, we use a DeepMedic model trained with the four input scan modalities named above. We replace the T2-weighted scan by the synthesized picture and evaluate the segmentations with respect to the tumor identification, using Dice scores as numerical evaluation. The evaluation shows that the segmentation works well with synthesized scans (in particular, with Pix2Pix methods) in many cases.